PRELIMINARY STUDY OF DIABETIC RETINOPATHY …eprints.utar.edu.my/3952/1/16ACB04891_FYP.pdf2.2.1 Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment with Image

PRELIMINARY STUDY OF DIABETIC RETINOPATHY CLASSIFICATION

FROM FUNDUS IMAGES USING DEEP LEARNING MODEL

BY

HOE YEAN SAM

A REPORT

SUBMITTED TO

Universiti Tunku Abdul Rahman

in partial fulfillment of the requirements

for the degree of

BACHELOR OF COMPUTER SCIENCE (HONS)

Faculty of Information and Communication Technology (Kampar Campus)

MAY 2020

REPORT STATUS DECLARATION FORM

BCS (Hons) Computer Science i

Faculty of Information and Communication Technology (Kampar Campus), UTAR

UNIVERSITI TUNKU ABDUL RAHMAN

REPORT STATUS DECLARATION FORM

Title: __________________________________________________________

__________________________________________________________

__________________________________________________________

Academic Session: _____________

I __________________________________________________________

(CAPITAL LETTER)

declare that I allow this Final Year Project Report to be kept in

Universiti Tunku Abdul Rahman Library subject to the regulations as follows:

1. The dissertation is a property of the Library.

2. The Library is allowed to make copies of this dissertation for academic purposes.

Verified by,

_________________________ _________________________

(Author’s signature) (Supervisor’s signature)

Address:

__________________________

__________________________ _________________________

__________________________ Supervisor’s name

Date: _____________________ Date: ____________________

Preliminary Study of Diabetic Retinopathy Classification from Fundus

Images using Deep Learning Model

May 2020

HOE YEAN SAM

15, Persiaran Gemilang 1,

Taman Gemilang, 35500

Bidor, Perak.

5/9/2020 5 September 2020

Sayed Ahmad Zikri Bin Sayed Aluwee

PRELIMINARY STUDY OF DIABETIC RETINOPATHY CLASSIFICATION

FROM FUNDUS IMAGES USING DEEP LEARNING MODEL

By

Hoe Yean Sam

A REPORT

SUBMITTED TO

Universiti Tunku Abdul Rahman

in partial fulfillment of the requirements

for the degree of

BACHELOR OF COMPUTER SCIENCE (HONS)

Faculty of Information and Communication Technology (Kampar Campus)

MAY 2020

DECLARATION OF ORIGINALITY

BCS (Hons) Computer Science iii



I declare that this report entitled “PRELIMINARY STUDY OF DIABETIC

RETINOPATHY CLASSIFICATION FROM FUNDUS IMAGES USING DEEP

LEARNING MODEL” is my own work except as cited in the references. The report

has not been accepted for any degree and is not being submitted concurrently in

candidature for any degree or other award.

Signature : _________________________

Name : _________________________

Date : _________________________


Hoe Yean Sam

5/9/2020

ACKNOWLEDGEMENTS

BCS (Hons) Computer Science iv


ACKNOWLEDGEMENTS

I would like to express my sincere thanks and appreciation to my supervisor, Dr. Sayed

Ahmad Zikri Bin Sayed Aluwee who has given me this bright opportunity to engage in

a deep learning project. It is my first step to establish a career in deep learning field. A

million thanks to you.

To a very special person in my life, Tang Mee Thye, for her patience, unconditional

support and love, and for standing by my side during hard times. Finally, I must say

thanks to my parents and my family for their love, support and continuous

encouragement throughout the course.

ACKNOWLEDGEMENTS

ABSTRACT

BCS (Hons) Computer Science v


ABSTRACT

Over the years, the cases of diabetes in Malaysia was increasing drastically. As a result,

diabetic retinopathy had emerged among the diabetic patients. Diabetic retinopathy was

a chronic eye disease that caused by diabetes, which would affect the eyesight and even

blindness. Despite the fact that the disease was becoming more common, doctors were

still conduct disease screening manually, which meant there was a risk of patients

diagnosed incorrectly. The doctors were still using the traditional method on the

diagnosis was because the lack of prediction data on diabetic retinopathy progression

locally. Eventually, researches on the diagnosis were difficult to be conducted.

Therefore, the preliminary study of the severity levels classification of diabetic

retinopathy from fundus images using deep learning model was introduced in this

project. Deep learning was a technique that could learn from the train fundus image

dataset and conduct prediction on the similar test dataset automatically. The model

architecture that used to train the dataset was DenseNet, which was a Convolutional

Neural Network (CNN) based architecture. In the development of this project, various

image pre-processing methods were done to enhance the image for training. Besides,

data validation and image transforming techniques including data augmentation and

test-time augmentation (TTA) were also used to evaluate training results and reduce

overfitting respectively.

The project involved the prediction testing on each image as well as the effects of data

augmentation and TTA by observing the quadratic weighted kappa values. At the end

of the project, a prediction model that able to predict and classify the severity labels of

fundus images was built using deep learning model. The prediction model had achieved

the quadratic weighted kappa score of 0.9308, whereas the overall accuracies attained

were higher than 74% (estimated) without TTA on APTOS test dataset and 65% on

Messidor-2 dataset, which were moderately accurate.

ABSTRACT

TABLE OF CONTENTS

BCS (Hons) Computer Science vi


TABLE OF CONTENTS

REPORT STATUS DECLARATION FORM i

TITLE PAGE ii

DECLARATION OF ORIGINALITY iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

TABLE OF CONTENTS vi

LIST OF FIGURES ix

LIST OF TABLES xii

LIST OF ABBREVIATIONS xiii

CHAPTER 1: INTRODUCTION 1

1.1 Problem statement 1

1.2 Background 1

1.3 Motivation 2

1.4 Objectives 3

1.5 Proposed approach 4

1.6 Report organisation 6

CHAPTER 2: LITERATURE REVIEW 7

2.1 Previous works on Deep Learning 7

2.1.1 U-Net: Convolutional Networks for Biomedical Image Segmentation 7

2.1.2 Development and Validation of a Deep Learning System for Diabetic

Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic

Populations With Diabetes 9

2.1.3 Automated Detection of Diabetic Retinopathy using Deep Learning 11

2.2 Previous works on image pre-processing 13

TABLE OF CONTENTS

BCS (Hons) Computer Science vii


2.2.1 Color Retinal Image Enhancement Based on Luminosity and Contrast

Adjustment with Image Fusion Technique 13

2.2.2 A Retinal Image Enhancement Technique for Blood Vessel

Segmentation Algorithm 15

CHAPTER 3: SYSTEM DESIGN 17

3.1 Project pre-development 18

3.2 Data pre-processing 26

3.3 Model training architecture building and Data training 36

3.4 Prediction on test dataset 42

CHAPTER 4: EXPERIMENTS AND RESULTS 44

4.1 Methodology 44

4.2 Tools and Requirements 45

4.3 Analysis 46

4.3.1 Model training 46

4.3.2 Post-training evaluation 51

4.3.3 Prediction testing 53

4.3.4 Verification of prediction results 57

CHAPTER 5: CONCLUSION 62

5.1 Project review 62

5.2 Problems encountered 63

5.3 Future work 65

5.4 Conclusion 66

BIBLIOGRAPHY 67

TABLE OF CONTENTS

BCS (Hons) Computer Science viii


APPENDIX A: POSTER A-1

APPENDIX B: PLAGIARISM CHECK RESULT B-1

APPENDIX C: FYP 2 CHECKLIST C-1

LIST OF FIGURES

BCS (Hons) Computer Science ix


LIST OF FIGURES

Figure Number Title Page

Figure 1-1 General development flow of the project. 4

Figure 2-1 U-net architecture. 7

Figure 2-2 Image before enhancement. 14

Figure 2-3 Image after enhancement. 14

Figure 2-4 Original image before SUACE. 15

Figure 2-5 Output image after SUACE. 15

Figure 3-1 Phases of project development. 17

Figure 3-2 The process flow of project pre-development phase. 18

Figure 3-3 Retina with no diabetic retinopathy. 20

Figure 3-4 Retina with mild diabetic retinopathy. 20

Figure 3-5 Retina with moderate diabetic retinopathy. 20

Figure 3-6 Retina with severe diabetic retinopathy. 20

Figure 3-7 Retina with proliferative diabetic retinopathy. 20

Figure 3-8 Sample image of APTOS. 21

Figure 3-9 Sample image of Messidor-2. 21

Figure 3-10 Records in train CSV file of APTOS. 22

Figure 3-11 Records in test CSV file of Messidor-2. 22

Figure 3-12 Bar chart of number of images to label distribution of

APTOS train dataset.

23

Figure 3-13 Test CSV after modification. 24

Figure 3-14 The process flow of data pre-processing phase. 26

Figure 3-15 The content of the CSV generated by find dark images

function.

27

Figure 3-16 Raw image before cropping. 27

Figure 3-17 Image after cropping. 28

Figure 3-18 Image after resized to 512x512. 28

Figure 3-19 Sample image after Gaussian Blur with SigmaX = 10. 29

Figure 3-20 Sample image after Gaussian Blur with SigmaX = 30. 30

Figure 3-21 Sample image in green channel. 31

LIST OF FIGURES

BCS (Hons) Computer Science x


Figure 3-22 Sample image applied with green channel extraction

and Gaussian Blur with sigmaX = 10.

31

Figure 3-23 Sample image applied with Gaussian Blur with sigmaX

= 10, followed by green channel extraction.

32

Figure 3-24 The Java-based code that contained the actions to be

done by ImageJ.

33

Figure 3-25 Sample of the output image by ImageJ. 33

Figure 3-26 Sample image of the output of SUACE. 34

Figure 3-27 224x224 sample image pre-processed with Gaussian

Blur with sigmaX = 10 that applied with Lanczos.

35

Figure 3-28 The process flow of model training architecture

building phase.

36

Figure 3-29 Summary of the model. 38

Figure 3-30 Images after data augmentation with number 5

combination.

41

Figure 3-31 The process flow of prediction on test dataset phase 42

Figure 4-1 General structure of DenseNet. 44

Figure 4-2 The information generated during training process. 46

Figure 4-3 The result of the dataset when the highest Kappa value

was attained during training.

47

Figure 4-4 Training details when the highest Kappa value was

attained, with the implementation of number 5 data

augmentation.

50

Figure 4-5 Line graph of mean square error (MSE) of loss versus

epoch.

51

Figure 4-6 Line graph of accuracy versus epoch in terms of

accuracy and validation accuracy.

51

Figure 4-7 Line graph of accuracy versus epoch in terms of kappa

score.

52

Figure 4-8 The progression of each severity level. 53

Figure 4-9 The format of prediction results in CSV. 53

Figure 4-10 Bar graph of the number of images versus the severity

labels of prediction without TTA.

54

LIST OF FIGURES

BCS (Hons) Computer Science xi



labels of prediction with TTA.

55

Figure 4-12 Test images from each predicted label from prediction

without TTA.

56

Figure 4-13 Test images from each predicted label from prediction

with TTA.

56

Figure 4-14 The format of prediction results in CSV (Messidor-2). 57


labels from the predicted results of Messidor-2.

58


labels from the actual results of Messidor-2.

59

Figure 4-17 Confusion matrix of actual and predicted results. 60

Figure 4-18 Information of the accuracy of the model on Messidor-

2 test dataset.

61

Figure 4-19 Images from Messidor-2 test dataset on each predicted

severity label.

61

Figure 5-1 The inconsistency of ophthalmologists on the

judgements on severity labels of a batch of images.

64

LIST OF TABLES

BCS (Hons) Computer Science xii


LIST OF TABLES

Table Number Title Page

Table 3-1 The table of numbered scales to the severity levels of

diabetic retinopathy and corresponded images.

20

Table 3-2 The tested combinations of data augmentation. 40

Table 4-1

Table 4-2

Table 4-3

Highest quadratic weighted kappa values achieved by

each pre-processed dataset.

Highest quadratic weighted kappa values achieved by

each data augmentation combination.

Overall accuracy of each threshold range in prediction

with and without TTA.

47

49

57

LIST OF ABBREVIATIONS

BCS (Hons) Computer Science xiii


LIST OF ABBREVIATIONS

CNN Convolutional Neural Network

TTA Test-time Augmentation

A.I Artificial Intelligence

APTOS Asia Pacific Tele-Ophthalmology Society

CLAHE Contrast Limited Adaptive Histogram Equalisation

SUACE Speeded Up Adaptive Contrast Enhancement

EDA

MSE

Exploratory Data Analysis

Mean Square Error

CHAPTER 1: INTRODUCTION

BCS (Hons) Computer Science 1


Chapter 1: Introduction

1.1 Problem statement

One of the problem statements of this project was a drastic increase in diabetes over

the years in Malaysia. According to the statistic Ministry of Health Malaysia, the

prevalence projection of diabetes was increasing drastically over the years with the

population of Malaysia (Bt. Ngah et al. 2017). On top of that, the current projection

already exceeded the reckoned projection. It was due to most of the Malaysians

received treatment solely at the primary level of healthcare, which led to ineffective

diabetes screening as they did not get the proper screening equipment to diagnose the

disease.

The next problem statement was lack of prediction data on diabetic retinopathy

progression in Malaysia. Although Malaysia ranked the top in diabetes rate in Asia

(Rakin 2018), it was difficult to find related researches or data on the prediction on

diabetic retinopathy. One of the reasons was the majority of diabetics were not

proactive in diabetic retinopathy screening since they did not become aware that

diabetes could cause this disease, making the prediction data extremely limited (Bt.

Ngah et al. 2017).

On the other hand, the next problem statement was that the manual prediction of

diabetic retinopathy was challenging. Diabetic retinopathy that found on each image

of the patient’s eyeball was incredibly tiny to detect and had the risk of diagnosing false

positive or negative screening results for some inexperience readers.

1.2 Background

Diabetic retinopathy was an eye disease that would affect the vision of eyes. It was

caused by diabetes, which was a condition whereby the blood glucose level exceeded

the normal level. The most natural thing was to apply for an eyes check-up as early as

possible to prevent this disease. Due to the rise of Artificial Intelligence (A.I), one of

the A.I technique called deep learning was involved in the prediction of diseases based

on medical images for more efficient use of resources as well as accurate results.

Therefore, understanding the diseases itself in the first place was required to have an




in-depth understanding of the relationship between these chronic diseases and how they

were being applied in deep learning.

Diabetic retinopathy was an eye disease, when a diabetic patient had an outrageously

high in the blood glucose level for an extended time, it caused damage to the blood

vessels of the retina and resulting in abnormal growth of blood vessels on the retina and

affects eyesight. The symptoms including poor night vision and blindness (Boyd 2019).

Diabetic retinopathy was a severe disease as it could result in malfunctioning of the

eyes. Therefore, the prediction of this disease by examining the retinal images was

carried out in a time interval fashion to identify the existence or severity of it, especially

for people with diabetes. In the past, detection of disease was solely conducted by

ophthalmologists, and they did it manually by examining the images merely by their

knowledge and judgements. It made the prediction process relatively inefficient since

the process was labour-intensive and might have made mistakes. The utilisation of deep

learning technique was one of the best solutions to automate the task while maintained

the precision and accuracy of the results.

Deep learning was a machine learning technique whereby the algorithms learned by

itself by studying the input dataset and build a prediction model due to deep learning

worked with a neural algorithm network, which mimicked the way of human think and

learned (Reyes 2020).

Deep learning was capable in speeding up the process of analysing and interpreting

large dataset (Brush et al. 2016). In terms of making a prediction based on images, deep

learning was capable of recognising a specific pattern and solved complex problems.

1.3 Motivation

The motivation behind this project was to develop a diabetic retinopathy prediction

tool that could assist doctors in referring to the detection results. It could increase

the efficiency of the diagnostic process drastically and enabled patients to receive early

detection on this disease.

Moreover, the project was motivated by the fact that Malaysia did not adopt the latest

technology like deep learning prediction in diabetic retinopathy screening. While

other countries like the United Kingdom and India were utilising deep learning




prediction technology in their healthcare, it was rare to see that healthcare of Malaysia

to acquire and made use of it to provide better diagnose and treatment to this disease.

Besides, the project was to automate the task of detection of diabetic retinopathy by

using deep learning technique, to reduce the labour of doctors while maintained the

certainty of results that were comparable to doctor’s judgement.

1.4 Objectives

The objective of this project was to analyse the suitable deep learning architecture

for the prediction of diabetic retinopathy. In deep learning, there were many

architectures, and each of them performed differently in solving problems. In the case

of the prediction of diabetic retinopathy, various type of deep learning techniques that

were suitable for image segmentation would be analysed to find the algorithm that

performed the best.

The next objective of this project was to conduct a prediction of diabetes retinopathy

using deep learning-based architecture. The project made used of the chosen deep

learning technique to train the dataset and construct a prediction model that contained

the information regarding the detection of diabetic retinopathy in the retinal images.

From the model, results would be validated and generated.

Furthermore, the project was to determine the severity levels of diabetic retinopathy

from the classification of the retinal images. By harnessing of the power of deep

learning, the classification of the severity levels of diabetic retinopathy would be

established, which categorised them into several levels including absence, moderate

and severe level that tell the risk of each patient. Information was relatively useful

because it could simplify the differentiation of each image in terms of severity as well

as showing the differences between each retinal image and the features that defined the

severities.




1.5 Proposed approach

Figure 1-1 General development flow of the project




Figure 1-1 illustrated the general development flow of this project. To kick start the

development of this project, datasets were sourced for model training. The datasets used

in this project were APTOS (Asia Pacific Tele-Ophthalmology Society) 2019 blindness

detection competition dataset for training and testing while Messidor-2 dataset for

further prediction testing and validation purpose since the testing labels for the APTOS

test dataset were unknown. Both APTOS and Messidor-2 contained fundus retinal

images that taken using fundus photography as well as CSV file that contained the

labels for each image with the range from 0 to 4 that represented the severity of the

diagnosis of the images, the higher the number the more serious the severity of the

disease. After that, the datasets were loaded into the temporary storage of Google Colab

from Google Drive.

Next, choosing model architecture for training was also a crucial step in the

development of this project. There were many types of model architecture for this

application. Hence, various types of deep learning network architectures were observed

and evaluated by referring to the quadratic weighted kappa value obtained that acted as

the evaluation parameter for prediction performance, the architecture that achieved

highest value would be chosen for this project, which was DenseNet.

In the core development of the project, the datasets were pre-processed with various

methods. Data pre-processing could help in enhancing the clarity and contrast of the

images. Besides, data augmentation and test-time augmentation (TTA) techniques were

introduced in this project to prevent overfitting of data training and improved the

prediction performance on test dataset respectively.

Then, followed by building model training architecture. This was the step whereby the

layers of the network, activation function, training batch size and epoch were defined

and ready to train the pre-processed dataset.

Furthermore, with the implementation of train/test split evaluation, the model was

evaluated by studying the train accuracy, train loss, validation accuracy, validation loss

and quadratic weighted kappa value generated by each epoch to observe the

performance during training and investigate the issue such as data overfitting. After that,

hyperparameters were tuned to increase the performance even further.

The entire process of data training was repeated until the highest quadratic weighted

kappa value was obtained. Then, the model was validated using the test dataset in order




to observe the performance of the model in terms of accuracy. All the implementations

in this project were done in Google Colab, which was a cloud-based Python notebook

with GPU acceleration enabled.

1.6 Report organisation

The chapter 1 of the project was the introduction. This section included the problem

statements, background and motivation, objectives as well as the proposed approach

for the development of this project.

Next, chapter 2 was the literature review of previous works regarding to the project.

This section included the previous works on deep learning and image pre-processing.

On top of that, the comparisons between previous works and the proposed study were

discussed as well.

Moreover, chapter 3 was the system design of the project. The system design was

categorised into different phases, which were project pre-development, data pre-

processing, model training architecture building and data training and prediction on test

dataset.

Furthermore, chapter 4 was the experiments and results achieved by the project. This

section had mentioned the methodology used and tools and requirements. Besides, the

analysis regarding to model training, post-training evaluation, prediction testing and

verification of prediction results were also discussed in details.

Lastly, chapter 5 was the conclusion of the project. This section included project review,

discussion of problems encountered, future work as well as the conclusion regarding to

the entire development of the project.

CHAPTER 2: LITERATURE REVIEW



Chapter 2: Literature Review

2.1 Previous works on Deep Learning

2.1.1 U-Net: Convolutional Networks for Biomedical Image Segmentation

(Olaf et al. 2015) proposed a research paper that was inspired by deep convolutional

network technique to conduct segmentation on biomedical images. Yet, the

development of this kind of technique was restricted by the size of the networks as well

as the available training dataset. Convolutional networks were usually used in

classification of images as a single class label but each class label required to be

assigned to each pixel of the image, this process was called localisation. On the contrary,

there was another paper proposed a sliding-window setup to conduct classification but

the construction of the network was rather slow and low in localisation accuracy and

contexts that allowed the network to examine.

From all these examples, this paper built a “fully convolutional network” named U-net

architecture that worked with limited training dataset while increased the precision of

segmentations. Figure 2-1 was the U-net architecture in overall.

Figure 2-1 U-net architecture

The downside of this architecture was GPU-intensive, especially for large dataset.

Hence, the memory of GPU would be the determining factor of the resolution of the




outputs. U-net architecture was applicable in various types of biomedical segmentations,

which made it relatively popular in healthcare recently.

This paper proposed a U-net approach to solve images segmentation problems. The

main difference of this paper from current project was they trained the dataset by using

local GPU whereby the current project preferred cloud service that was GPU supported.

This method was extremely costly, although it could speed up the training process

drastically and made training done efficiently.

Since the current approach in this project was to utilise the GPU that was built into

Google Colab, which was only offered rather limited GPU power, the U-net was not

practical for cloud-based training as the limited resources would not be able to handle

such GPU-intensive task, which eventually would hit the bottleneck of the GPU easily.




2.1.2 Development and Validation of a Deep Learning System for Diabetic

Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic

Populations With Diabetes

(Quelleca et al. 2017) referred and inspired by 2015 Kaggle Diabetic Retinopathy

competition, which was a deep learning competition that was to create a system that

could automate the detection of diabetic retinopathy with given retinal images. This

paper investigated the solutions that ranked top in the leader board and the solutions

were using an artificial neural network called ConvNets, which also known as

Convolutional Neural Network (CNN), to build the prediction model. However, this

paper mentioned that this technique was not trustable by professional physicians. Hence,

a new solution was proposed by creating a heatmap from the prediction referable

diabetic retinopathy (moderate or higher severity levels) and its features by pixels. The

paper proposed a modified version of ConvNet that integrated non-mydriatic

retinographs and algorithms to automate the diagnosis of this disease. It also mentioned

that although the solution was questioned on replacing manual detection of this disease

and only suitable for reference, it had the potential on discovering new findings from

the images.

In this paper, the prediction was solely on referable diabetic retinopathy images as it

was the most meaningful context as it determined the decision on whether the diabetics

should refer to an ophthalmologist. However, some of the patients that were neared to

moderate severity level might not got the opportunity to receive treatments and missed

out the golden timing on getting medications. Attentions should be given to all kind of

severity levels in order to provide a holistic severities reference and made early

diagnosis truly purposeful. On another hand, the solution presented in form of heat

map that ease in identifying the severity and it was able to be applied to majority of the

relevant problems as well, which could save a lot of time as compared to developing

the algorithm from ground up.

Overall, the proposed solution from the paper was based on CNN due to its promising

performance. Despite the fact that the solution did not required expert knowledge in the

field of diabetic retinopathy and only needs to refer the decisions based on evaluation

records, it still required experts in image segmentation of this disease to further

optimised and tuned the detection in order to improve its overall performance. On top

of that, the training dataset that was used in this paper was not entirely developed by




retinal professionals, which meant that not all images were graded by retinal specialist.

Ultimately, this would affect the predicting performance in real world. In addition, the

identification of diabetic retinopathy traits still required clinical examination in order

to identify the possible cases of this disease from the fundus images.

On another hand, this paper proposed a heatmap approach to detect and classify the

severity of diabetic retinopathy, whereby the current project solely focused on

classifying the diagnosed severity of the disease and output the result into a CSV file.

Besides, the results generated required to be verified by specialists. In order to create

the model that performed the best, the project would only use specialist verified datasets.

In other words, all datasets used in this project were graded by professional

ophthalmologists.




2.1.3 Automated Detection of Diabetic Retinopathy using Deep Learning

(Lam et al. 2018) demonstrated the automated detection of diabetic retinopathy based

on Convolutional Neural Network (CNN), which was one of the deep learning

techniques. This paper achieved 95% of validation sensitivity during performance

testing. In addition, transferred learning on other CNN architectures including

GoogLeNet and AlexNet pretrained models from ImageNet to determine the best

performing architecture for prediction on this disease. In terms of dataset, the paper

used retinal images that obtained from Kaggle with stated severity levels that were

classified into 5 classes, from normal to end stage. Besides, the project also used

Messidor-1 dataset that was verified by physician for the algorithms.

Pre-processing of images and data augmentation were introduced in the paper. For pre-

processing, the images were cropped into size of 256x256 and extracted the retina by

using method of Otsu, followed by normalising the images. Next, this paper utilised

Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm to adjust the

contrast of images. On another hand, data augmentation was done with the padding of

zeroes, zoom, rolling and rotation to reduce overfitting of data and improve localisation

of network.

The reason that this paper used architectures from ImageNet was that it was widely

used and optimised to detect features in biological images. Despite the fact that CNN

was able to achieve high variance and low bias in the prediction models, the increased

of dataset classes or multi-stage classification would cause the decreasing in

performance of the models. Besides, this paper also mentioned that CNN was unable to

detect obscure features and caused the incorrect classification of the images between

normal and mild severity. Therefore, verification by experts would be required to

improve the performance in this particular classification. Furthermore, the paper stated

that the input data was insufficient for prediction as CNN required vast size of data to

perform better with high detection accuracy. The model of GPU used was Tesla K80.

The good thing about this paper was that the alternatives of CNN-based architectures

were tested by using transfer learning method to find out the suitable technique for the

prediction of this disease as well as improved the pre-trained model even further.

Besides, the network was validated further by using the physician verified dataset to

make sure it could be used with various sources of the dataset of retinal images.




Overall, the approach of this paper was rather similar to the current development of this

project, including the number of severity level classes, reduction of image sizes, GPU

model, utilisation of data augmentation technique and physicians verified dataset and

limitations mentioned in this paper such as the insufficient of input data. The

insufficient of input data was inevitable considering the limitations in the insufficient

available resources in terms of GPU power and storage as well as the availability and

reliability of the sources of dataset. On another hand, the network used for this project

was DenseNet, which resulted in great performance, even with limited size of input

dataset and computing power. Furthermore, the CLAHE algorithm that used in the

paper was tested this this project as well, turned out dataset with normalised colouration

worked the best since CLAHE algorithm not only adjusted the contrast of image details

but also the noises.




2.2 Previous works on image pre-processing

2.2.1 Color Retinal Image Enhancement Based on Luminosity and Contrast

Adjustment with Image Fusion Technique

(Vanmathi & Devarajan 2017) were inspired by the fact that the algorithms that classify

the types of diabetes required the input of high definition retinal images. Therefore, the

enhancement of distorted or low-quality images were necessary via some image

enhancing algorithms to avoid false diagnosis. In this proposed paper, some retinal

image enhancing techniques were introduced including Luminance enhancement,

Image Fusion as well as Contrast Limited Adaptive Histogram Equalisation (CLAHE).

The difficulties in extracting necessary traits from images were mainly due to the

presence of image noises and low image contrast, which eventually made the

diagnosing process even challenging for retinal specialists.

Image Fusion was a technique that combined the images with different focus point,

which could retain information in the image. Moreover, the paper also implemented

RGB Extraction. This method was to extract the colour of an image into different colour

channels, which were red, green and blue colour channel, more colour planes aided in

enhancement of images. On another hand, due to the uneven distribution of luminosity

of images, Luminosity Enhancement was introduced to make the overall of images had

the balanced luminosity. In order to improve the clarity of images even further, CLAHE

was used to enhance the contrast on grey images as well as reduce the distortion of

coloured images. By applying these methods, the enhancement of the retinal structures

yet retained the raw information in the images were achieved. Figure 2-2 and Figure 2-

3 was the comparison of retinal image before and after enhancement respectively.




Figure 2-2 Image before enhancement Figure 2-3 Image after enhancement

This paper proposed a rather effective and interesting image enhancement methods, but

the entire process was relatively time consuming for this project as some of algorithms

required detailed investigations and tuning to obtain the best enhancement parameters

for a particular dataset. Therefore, the current project applied prebuilt algorithms that

suitable for the dataset chosen such as retinal image cropping and gaussian blur to

enhance the clarity and contrast of the fundus images.




2.2.2 A Retinal Image Enhancement Technique for Blood Vessel Segmentation

Algorithm

Segmentation of retinal images was a process that involved the segmenting of blood

vessel from the background. (Bandara & Giragama) had proposed an algorithm to

improve the accuracy of image segmentation by improving the quality of retinal images.

In order to produce high quality image, the contrast must be consistent throughout the

entire image. There were many contrast enhancing methods such as contrast limited

adaptive histogram equalisation (CLAHE), but this method would erase important

details and made unwanted details significant instead.

Hence, this paper proposed a method named Speeded Up Adaptive Contrast

Enhancement (SUACE) to solve this problem in the segmentation of blood vessels from

retinal images. This method would convert image into greyscale and transformed noises

looked like discontinuation from the blood vessels in order to ease the removal of noises.

Figure 2-4 and Figure 2-5 showed the original image and the output image after SUACE

respectively.

Figure 2-4 Original image before SUACE Figure 2-5 Output image after SUACE

Despite this method was capable in enhancing the quality of retinal images, SUACE

was not suitable in the application of diabetic retinopathy prediction. This was because

the application of current project was focusing on the features of this disease, which

mostly scattered throughout the entire surface, while this paper was solely focused on

the clarity of blood vessels. Therefore, this technique would had suppressed some of

the important features that were related to the disease. For the current project, a contrast

enhancement of coloured image would be more effective in highlighting the features of




diabetic retinopathy as well as better presentation. In order to achieve this, gaussian

blur technique was implemented to enhance the image contrast and normalised the

overall colouration of images.

CHAPTER 3: SYSTEM DESIGN



Chapter 3: System Design

The processes of the project were categorised into different phases in the development,

which were project pre-development, data pre-processing, model training architecture

building and data training, and prediction on test dataset. Figure 3-1 illustrated the

phases of the development. The implementations of this project were done in Google

Colab with Python and GPU acceleration enabled.

Figure 3-1 Phases of project development




3.1 Project pre-development

Figure 3-2 showed the process flow of pre-development phase and each process inside

the flow was explained in details.

Figure 3-2 The process flow of project pre-development phase




• Data sourcing

This was the phase whereby the datasets of the fundus images of retina for this

project were identified and acquired. There were 2 datasets required, which were

training and testing dataset. The training dataset would be the input dataset in

training phase to conduct the training of the data via deep learning network in order

to create a prediction model. On another hand, the testing dataset would be used to

evaluate and validate the performance of the classification. Besides, the severity

label of the disease for each image was also required in data training and

performance evaluation of the project. The labels were scaled from 0 to 4, each of

the scale represented the severity of the diabetic retinopathy disease of a retinal

image, the higher the scale, the more severe of the disease. Table 3-1 showed the

severity and image of diabetic retinopathy for each scale.




Scale Severity level of diabetic retinopathy and image

0 No

Figure 3-3 Retina with no diabetic retinopathy

1 Mild

Figure 3-4 Retina with mild diabetic retinopathy

2 Moderate

Figure 3-5 Retina with moderate diabetic retinopathy

3 Severe

Figure 3-6 Retina with severe diabetic retinopathy

4 Proliferative

Figure 3-7 Retina with proliferative diabetic retinopathy

Table 3-1 The table of numbered scales to the severity levels of diabetic

retinopathy and corresponded images




In order to fulfil these criteria, APTOS (Asia Pacific Tele-Ophthalmology Society)

2019 blindness detection dataset, which was a Kaggle’s competition dataset, was

acquired for the development. APTOS dataset had both training and testing datasets

that contained the filename of the fundus retinal images with the severity labels of

diagnosis of the images in separated train and test CSV files, but only the training

images labelled with severity scales. The dataset contained 3663 and 1928 coloured

train and test images respectively, which contributed 9.52GB of data size. Due to

the fact that only the training dataset was labelled with severity, the testing dataset

could not be used for validation because the actual severity of each test image was

unknown. On top of that, the severity labels in the test CSV file were not published

for the public even the competition was ended, which made the evaluation and

validation of the result unable to be conducted for this project. Therefore, another

dataset with the similar fundus images and labels needed to be acquired to act as

the test dataset for this project.

Hence, Messidor-2 dataset was acquired to act as the second test dataset. The dataset

contained 1748 coloured fundus retinal images, which contributed 2.30GB of data

size. However, Messidor-2 did not come with the severity labels in the CSV file

together with the images. Therefore, the CSV file with the labels was downloaded

from Kaggle dataset repository, which graded by retina specialists. Since the train

and test datasets were from different sources, the simulation of real-world

conditions during the evaluation of the prediction results could be achieved as there

were many types of fundus retinal images from different sources in the actual world.

Figure 3-8, Figure 3-9, Figure 3-10 and Figure 3-11 showed the sample image of

APTOS, sample image of Messidor-2, records in train CSV file of APTOS and

records in test CSV file of Messidor-2 respectively.

Figure 3-8 Sample image of APTOS Figure 3-9 Sample image of Messidor-2




Figure 3-10 Records in train CSV file of APTOS

Figure 3-11 Records in test CSV file of Messidor-2

• Data loading

The datasets were uploaded into Google Drive and transferred the data to Google

Colab. The purpose of doing this was because Google Colab did not support

persistent storage, therefore Google Drive would be integrated with Google Colab

and became the alternate persistent storage for Google Colab. The method was able

to speed up the transfer speed tremendously between Google Drive and Google

Colab because both platforms were cloud-based platform, which meant that the data

was transferred from cloud to cloud. On the contrary, manually upload from local

machine to Google Colab would be extremely time consuming as this scenario was

from local to cloud.

• Data labelling

The CSV files were transformed into data frames that labelled the images in in terms

of the name of the image and severity level accordingly.




• Exploratory data analysis (EDA)

The purpose of having EDA was to explore and understand the data before any

modification or development done to the dataset. The main feature to look for in

the dataset was to study the number of images on each severity level in order to

observe the overall distribution of the number of labels on train dataset. Figure 3-

12 illustrated the bar chart of number of images to label distribution of APTOS train

dataset.

Figure 3-12 Bar chart of number of images to label distribution of APTOS train

dataset

In Figure 3-12, the labels on number of images from the most to least were label 0,

2, 1, 4 and finally 3. Label 0 was significantly more than the other labels, which

meant there was a drastic difference in terms of number of images between label 0

and other labels. Such difference showed that the dataset was imbalanced in

distribution of labels.

Furthermore, the resolution of each images was noted in order to consider whether

resizing was needed to prevent resource exhaustion as well as the samples of dataset

were displayed to make sure the images were loaded correctly.




Next, in order to ensure the images were able to be loaded by matching the filename

records in the CSV file, the records were checked to have the same filenames with

the filenames of the images. On top of that, the generic declaration on the formats

of the filenames were also studied in order to create a function to load all the images

automatically.

From the Figure 3-11, there were more columns in the test CSV of Messidor-2

compared to APTOS train CSV (Figure 3-10). Hence, the test CSV required to be

modified to match the numbers of columns in the train CSV. The

adjudicated_dr_grade was same as the diagnosis column in the train CSV, which

represented the grade or severity scale of diabetic retinopathy of the images. The

additional columns were adjudicated_dme and adjudicated_gradable. The column

of adjudicated_dme represented the referable diabetic macular edema. This

particular column would be removed because it was not the feature required in this

project. On another hand, adjudicated_gradable represented the image quality grade,

1 meant gradable while 0 meant ungradable. Therefore, the rows with 0

adjudicated_gradable would be removed since the adjudicated_dr_grade was empty,

which meant the image was not gradable and unable to be used for evaluation

purpose. Ultimately, there were 1744 images would be used as test dataset. After

that, the entire adjudicated_gradable column was removed as well. Figure 3-13

showed the modified test CSV.

Figure 3-13 Test CSV after modification




• Identify and evaluate model architecture

In this process, various types of model architecture were identified and evaluated in

order to find the suitable network architecture for this project. The repositories of

notebooks regarding to the APTOS competition were referred on GitHub and

investigated the quadratic weighted kappa values that achieved by different network

architectures that tested by the notebook authors. Hence, the quadratic weighted

kappa value would be acted as the evaluation parameter that described the

prediction performance of the architectures.

• Selecting the model architecture for development

After the model architectures were identified and evaluated, the architecture that

achieved the highest quadratic weighted kappa would be chosen for the

development of this project, which was a CNN-based architecture called DenseNet.




3.2 Data pre-processing

The reason of conducting data pre-processing was due to the fact that not all images

were perfectly captured, some images were not preferable due to certain circumstances

such as noises, unnecessary background, overexposed and underexposed. Hence, it

could help in enhancing the images in terms of contrast and clarity. Figure 3-14 showed

the process flow of data pre-processing phase and each process inside the flow was

explained in details. In this phase, libraries regarding to computer vision including

Pillow and cv2 were used to allow the system to recognise image format and made

modifications on the images.

Figure 3-14 The process flow of data pre-processing phase




• Find dark Images

This function was to identify the dark images in the dataset, which were not usable

for training because the low-quality images would provide the incorrect information

when the network was learning the data and affected the performance of the

prediction model. A CSV file would be generated after the identification of dark or

unusable images. The file had the “black” column that listed the type of the images,

0 was not dark while 1 was dark image. In APTOS dataset, all images were usable.

Figure 3-15 showed the content of the CSV generated by the function. Messidor-2

dataset would not require to be in this function because the CSV already stated the

gradeability of the images.

Figure 3-15 The content of the CSV generated by find dark images function

• Image borders cropping

The purpose of implementing this function was to crop the excessive dark borders

in the images. This was due to the fact that only the retina was necessary for the

prediction and the dark borders would become an unwanted details or noise in the

image. Figure 3-16 illustrated the raw image before cropping and Figure 3-17

illustrated the image after cropping.

Figure 3-16 Raw image before cropping




Figure 3-17 Image after cropping

• Image resizing

The images were resized to dimensions of 512x512, which was one of the common

choices in image-based deep learning domain. Resizing the images could unify the

dimensions, which turned out all the images were squared. Besides, resizing could

also downsizing the resolution of the images in order to reduce the consumption of

computational resources to avoid system crashing. Figure 3-18 showed the image

after resized to 512x512.

Figure 3-18 Image after resized to 512x512




• Various image enhancements

In order to determine the best image enhancement method for the project, trial and

error of different types of image enhancing methods were applied to the train and

test datasets. The following were the methods applied in enhancing the images:

a) Gaussian Blur with sigmaX = 10

This method was able to enhance the overall contrast of the image and made

the features of the retina more apparent including blood vessels and diabetic

retinopathy spots such as exudates. Besides, Gaussian Blur normalised the

colour of images to ensure the pixels of the images had similar distribution

(B 2017). The parameter sigmaX was the standard deviation in X-axis

direction of the Gaussian kernel. The kernel was implemented from a cv2

library.

In fact, this method was used together with images borders cropping and

packaged as Ben’s Pre-processing in other sources. Figure 3-19 was the

sample image after pre-processed with Gaussian Blur with sigmaX = 10.

Figure 3-19 Sample image after Gaussian Blur with sigmaX = 10




b) Gaussian Blur with sigmaX = 30

This was a similar process of Gaussian Blur, except that sigmaX = 30

parameter was set.

The processed image was yellowish in colour and brighter compared to

sigmax = 10. Figure 3-20 showed the sample image after Gaussian Blur with

sigmaX = 30.

Figure 3-20 Sample image after Gaussian Blur with sigmaX = 30

c) Green channel extraction

The images in the project were made up by 3 colour channels, which were

red, green and blue colour. Among these colour channels, the green channel

was able to provide a better contrast in illustrating the image details (Sisodia,

Nair & Khobragade 2017). Hence, contrast limited adaptive histogram

equalisation (CLAHE) was applied to extract the green channel from the

images, which resulted the images appeared to be greyscale. In addition, the

network architecture required 3 colour channels but the processed images

only had 1 channel. Therefore, merging of the same colour channels was

required to make the images contained 3 channels without distorting the

images. Figure 3-21 showed sample image in green channel.




Figure 3-21 Sample image in green channel

d) Green channel extraction, followed by Gaussian Blur with sigmaX = 10

Figure 3-22 showed the sample image applied with green channel extraction

and Gaussian Blur with sigmaX = 10. In figure 3-22, the image appeared to

be overexposed and made the noises more apparent.

Figure 3-22 Sample image applied with green channel extraction and

Gaussian Blur with sigmaX = 10




e) Gaussian Blur with sigmaX = 10, followed by green channel extraction

Figure 3-23 showed the sample image applied with Gaussian Blur with

sigmaX = 10, followed by green channel extraction. In figure 3-23, the

image appeared to be slightly brighter than the one with green channel

extraction only.

Figure 3-23 Sample image applied with Gaussian Blur with sigmaX = 10,

followed by green channel extraction

f) External pre-processing with ImageJ

Instead of processing the datasets in Google Colab, external processing by

utilising ImageJ, which was an image processing program, was done to

enhance the images on local machine. The reason behind this was because

ImageJ had the features that were not yet developed in Python and more

user friendly. The enhancements done to the datasets in sequence were:

i. Channels splitting

ii. Green channel was selected

iii. Sharpening

iv. “Northeast” shadows were applied

In order to automate the enhancements in intended sequences, an ImageJ

built-in feature was used, called Macros. By using this feature, a generic

Java-based sequences were written to allow the program to read all the

images and apply the enhancements accordingly. Figure 3-24 and Figure 3-




25 showed the Java-based code that contained the actions to be done by

ImageJ and the sample of output image by ImageJ.

Figure 3-24 The Java-based code that contained the actions to be done by

ImageJ

Figure 3-25 Sample of the output image by ImageJ




g) Speeded Up Adaptive Contrast Enhancement (SUACE)

This method was inspired by a paper proposed in literature review and the

source codes were available to the public on GitHub. This method was based

on C++ and OpenCV to conduct retinal image enhancement. Since this

method would be ran in local machine, C++ compiler and OpenCV library

for C++ must be available. This method was initially developed to enhance

the details of blood vessels in the retina, but the author mentioned that this

method was also applicable to other image enhancement applications

because the SUACE was an algorithm for enhancing the contrast of image.

Figure 3-26 showed the sample image of the output of SUACE. In Figure

3-26, the image appeared to be flattened and smoothen some noises, yet also

removed some of the important details including the features of diabetic

retinopathy.

Figure 3-26 Sample image of the output of SUACE

• Create and upload pre-processed datasets

After the pre-processing of images were completed, the images would be outputted

and packaged as a dataset. Then, the prepared datasets were uploaded to Google

Drive and readied for training.




• Image resizing (224x224) [optional]

The images were further resized to 224x224 due to the limitations of RAM and

GPU memory in Google Colab that unable to compute 512x512 dataset. In order to

prevent session crashing due to lack of RAM and GPU memory, reduction of the

dimensions of images was necessary in reducing the usage of resources. Since the

reduction of image dimension would also reduce image resolution, this process was

optional.

• Applying Lanczos filter [optional]

This was an optional process. Lanczos was an anti-aliasing filter to “smooth” out

the edge of the pixels. The application of the filter was an optional process because

Lanczos showed no noticeable difference in presentation compared to the image

without the filter. Therefore, it was better to compare the results of prediction with

and without the filter. Figure 3-27 showed the 224x224 sample image pre-processed

with Gaussian Blur with sigmaX = 10 that applied with Lanczos.

The process was allocated after the creation of datasets because the filter was for

trial and error and comparison purpose, hence another creation of dataset was not

necessary.

Figure 3-27 224x224 sample image pre-processed with Gaussian Blur with

sigmaX = 10 that applied with Lanczos




3.3 Model training architecture building and Data training

The packages used in development of the architecture included TensorFlow, Keras and

Scikit-learn. Figure 3-28 showed the process flow of model training architecture

building and data training phase and each process inside the flow was explained in

details.

Figure 3-28 The process flow of model training architecture building phase




• Creating train and test arrays

The x_train, y_train and x_test arrays were defined, whereby x was the dataset and

y were the labels of the dataset.

• Transforming labels to multi-labels

Each of the y_train label array was in the format of [0 0 1 0 0] by default, whereby

the sequence location of “1” indicated the severity of the disease. For instance, in

[0 0 1 0 0], “1” was located at third place among the zeroes, which indicated

moderate severity of the disease of an image. The problem of this kind of labelling,

which was single labelling, was that there was no evidence on the progression of

the severity scales. Single labelling also carried lesser information since the labels

treated the problem as binary classification, which meant that the classification was

either no or certain level of severity. Hence, the labels were transformed to multi-

labels, which resulted in array format of [1 1 1 0 0]. The “1” in multi-labels were

filling the previous zeroes and went until certain level. In multi-labels, the

progression until certain level of severity was shown and the progression could be

treated as the indication of indexes of each level.

• Train/test data splitting for validation

Train/test data splitting technique was used in order to carry out model validation

during data training. The train dataset was split into 85/15 for validation, which

indicated 85% of data for training while 15% was for testing. This technique was

relatively useful in generating the validation accuracy and loss in each training

epoch during data training. Besides, this type of validation technique also consumed

lesser resources.

• Defining model architecture

This process was to initialise the model architecture. In this project, DenseNet121

was defined with the weights from ImageNet and without the fully-connected layer

at the top of the model in order to take in custom input shape. Since the input dataset

was resized to 224x224 and contained 3 channels, the input shape would be defined

as (224, 224, 3).




Next, the layers of the network were defined and built as sequential. The activation

function of the activation layer was sigmoid. This was due to the fact that this

project was deemed as a multi-label classification and sigmoid was able to produce

independent probabilities of each of severity level in an array (Draelos 2019). This

allowed the observation of the progression of the indexes of each level.

Furthermore, the model was compiled with the loss function of binary cross-entropy,

optimizer instance of Adam algorithm with learning rate of 0.00005 and prediction

metrics in accuracy. Figure 3-29 listed the summary of the model.

Figure 3-29 Summary of the model

• Creating training call-back metrics

The purpose of creating call-back metrics was to define the actions and metric to be

computed at the end of each epoch. When an epoch was completed, the call-back

function would compute and generate the quadratic weighted kappa score that

indicated the performance of the model. If the quadratic weighted kappa value

model in current epoch higher than the last one, the function would save the model

in h5 format, which would be used in prediction testing.




• Data augmentation

Data augmentation was a technique to transform the data to increase the amount of

data, which eventually diversified the data. This method could avoid overfitting of

the data and results in higher accuracy and lower loss of the prediction model.

Hence, a function called ImageDataGenerator was implemented to achieve this.

There were various transforming combinations to transform the data. In this project,

5 different combinations of data augmentation were tested with batch size of 32 in

order to obtain the most suitable combination of data transformation. Table 3-2

listed the tested data augmentations and Figure 3-30 showed the images after data

augmentation with number 5 combination from Table 3-2.




Number Combinations of data transforming functions

1 zoom_range = 0.15,

fill_mode = ’constant’,

cval = 0.,

horizontal_flip = True,

vertical_flip = True

2 rotation_range = 360,

brightness_range = [0.5, 1.5],

zoom_range = [1, 1.2],

zca_whitening = True,


vertical_flip = True,

fill_mode = ‘constant’

3 featurewise_center = True,

featurewise_std_normalization = True,

rotation_range = 20,

width_shift_range = 0.2,

height_shift_range = 0.2,

horizontal_flip = True



fill_mode = ‘nearest’,

zoom_range = 0.1,

rotation_range = 45





height_shift_range = 0.2

Table 3-2 The tested combinations of data augmentation




Figure 3-30 Images after data augmentation with number 5 combination

• Model training

This process was to execute the training of the model after the architecture for

model training was built. When the model was trained for the first time, 50 epochs

was chosen to observe the loss, accuracy, quadratic weighted kappa values and

overall performance the of training in order to determine whether the training had

the potential to improve further. In order to ensure the training had achieve the best

possible result, the epoch number was increased to 150 to allow the data had more

times to pass through the training process. The number of steps in an epoch was set

as the number of rows in split train dataset divided by the batch size.




3.4 Prediction on test dataset

Figure 3-31 showed the process flow of prediction on test dataset phase and each

process inside the flow was explained in details.

Figure 3-31 The process flow of prediction on test dataset phase

• Loading model

After the training was completed, the saved model would be loaded.

• Test-test augmentation (TTA)

TTA was a data augmentation technique that transformed the test dataset. In this

project, TTA was done by using the combinations configured previously in data

augmentation. TTA was set with 6 steps, which indicated that the test datasets

would be went through TTA for 6 times. The steps were already the maximum

in this development due to the limitation of RAM. TTA was referred as the

alternative path before predicting the test dataset because there was no guarantee

that TTA could help in improving the performance, or worsen the performance

instead. Therefore, the prediction was done with and without the TTA in order

to test and compare both output results to investigate that which performed the

best.




• Predicting test dataset

The loaded model would be used to predict both APTOS and Messidor-2 test

datasets. The prediction threshold was set at greater than 0.5, which meant that

only the predicted severity result that exceeded the prediction index or threshold

of 0.5 would be counted. In other words, the prediction outcomes would be

confirmed as the final results if the model had the confidence that was greater

than 50% in terms of the prediction index of each level. Due to this, the

threshold could be deemed as the adjustable prediction sensitivity parameter.

After the prediction results were generated, the results would be appended into

the test CSV and outputted as another result CSV.

CHAPTER 4: EXPERIMENTS AND RESULTS



Chapter 4: Experiments and Results

4.1 Methodology

The network architecture utilised in this project was DenseNet. DenseNet was one of

the recent Convolutional Neural Network (CNN). In many CNN-based architectures,

there was a case where the input information in the network would be disappeared as it

approached the end. Therefore, DensseNet tackled this problem by connecting each

layer to other layers as well in a feed-forward pattern (Chablani 2017). Each of the layer

received inputs from every layer in the network and dedicated the features of the layer

itself to other layers at the same time. In other words, all the layers had concatenated

the information from other layers in the network in order to maximise the information

flow in network. For illustration, Figure 4-1 illustrated the structure of DenseNet

architecture (Tsang 2018).

Figure 4-1 General structure of DenseNet

As a result, DenseNet was able to reduce the number of channels and parameters, which

eliminated the relearning of redundant features from each layer. On top of that,

DenseNet could also reduce overfitting on smaller dataset on training as well as

increased the efficiency of the model computation by making the network more

compact in general (Huang et al. 2018).




4.2 Tools and Requirements

1. Asus X556UF laptop with specifications:

• Processor: Intel Core-i5-6200U 2.30Ghz

• GPU: Nvidia Geforce 930M with 2GB memory

• Memory: 8GB RAM

• Storage: 265GB SSD

• Operating system: Windows 10

2. Google Colaboratory (Colab) on Google Chrome with specifications:

• Processor: Intel Xeon 2.30Ghz

• GPU: Tesla K80 with 11.4GB memory

• Memory: 13GB RAM

• Storage: 35GB

3. Google Drive

4. ImageJ 1.53c

5. Microsoft Visual Studio 2015 with C++ compiler and OpenCV library

6. Sublime Text 3




4.3 Analysis

4.3.1 Model training

Figure 4-2 showed the information generated during the execution of training process.

Figure 4-2 The information generated during training process

In Figure 4-2, the number 98/97 that highlighted in red colour rectangle was the number

of steps in an epoch. This meant that the training process was progressing in dividing

the image dataset into 98 batches, and each batch contained 32 images to allow it trained

the dataset by batches in order to prevent the system from overwhelming. With the

implementation of train/test split data validation method, the details about the loss,

accuracy, validation loss and validation accuracy were generated, which highlighted in

orange colour rectangle. The accuracy referred to the accuracy of the model on split

train dataset on training while validation accuracy was the accuracy on the split test

dataset on model validation, which could be referred as the expected accuracy. By

having the same concept, loss was referred to the error of the model on training while

validation loss was the error on model validation. The purpose of observing these values

was to detect overfitting of the dataset.

On another hand, the validation kappa value that highlighted in green rectangle was

acted as the indication or benchmark of the model performance. This was because

kappa was to measure accuracy of classification and more suitable in solving problems

with imbalance class (Brownlee 2016). On top of that, Kappa was a measurement that

compared the accuracy and the validation accuracy in order to measure the closeness

of the results of predicted instances from the ground truth. Once a trained model with

higher validation kappa value was generated, that particular model would be saved and

overwrote the previous model (if any), which would be used for evaluation and severity

labels prediction.




In order to obtain the best model, the created pre-processed datasets with number 1 data

augmentation combination from Table 3-2 were trained in order to generate a model.

The raw dataset with the same data augmentation combination was also trained to act

as control. Table 4-1 was the highest quadratic weighted kappa values achieved by each

pre-processed dataset.

Number Pre-processed dataset Highest quadratic

weighted kappa

value obtained

1 Gaussian Blur with sigmaX = 10 0.9288

2 Gaussian Blur with sigmaX = 10, followed by Lanczos

filter

0.9231

3 Gaussian Blur with sigmaX = 30 0.9271

4 Green channel extraction 0.9172

5 Green channel extraction, followed by Gaussian Blur with

sigmaX = 10

0.9144

6 Gaussian Blur with sigmaX = 10, followed by green

channel extraction

0.9217

7 External pre-processing with ImageJ 0.8650

8 Speeded Up Adaptive Contrast Enhancement (SUACE) 0.8018

9 Raw dataset 0.9265

Table 4-1 Highest quadratic weighted kappa values achieved by each pre-processed

dataset

From Table 4-1, the model trained with dataset that pre-processed with Gaussian Blur

with sigmaX = 10 had obtained the highest quadratic weighted kappa in comparison, at

0.9288. Hence, the dataset would be selected for further analysis. In addition, Figure 4-

3 showed the result of the dataset when it attained the highest Kappa value during

training and the validation loss (highlighted in orange colour rectangle) was slightly

high in value, at 0.1192. This was an indication of overfitting.

Figure 4-3 The result of the dataset when the highest Kappa value was attained during

training




Next, each data augmentation combination was used to transform the previously

selected dataset and it was put on training to generate a model in order to investigate

which combination achieved the highest quadratic weighted kappa value. Table 4-2

listed the highest quadratic weighted kappa values achieved by each data augmentation

combination on the dataset.




Number Combinations of data transforming

functions

Highest quadratic weighted

kappa value obtained

1 zoom_range = 0.15,

fill_mode = ’constant’,

cval = 0.,


vertical_flip = True

0.9288


brightness_range = [0.5, 1.5],

zoom_range = [1, 1.2],

zca_whitening = True,



fill_mode = ‘constant’

0.9241


featurewise_std_normalization = True,

rotation_range = 20,


height_shift_range = 0.2,

horizontal_flip = True

0.9265



fill_mode = ‘nearest’,

zoom_range = 0.1,

rotation_range = 45

0.9161





height_shift_range = 0.2

0.9308

Table 4-2 Highest quadratic weighted kappa values achieved by each data

augmentation combination

From Table 4-2, the combination number 5 obtained the highest quadratic weighted

kappa value in comparison, at 0.9308. In addition, Figure 4-4 showed the training

details when the highest Kappa value was attained. The figure showed that the current




approach was still had slight overfitting, because the validation loss (highlighted in

orange colour rectangle) was still considered a bit high although it was slightly reduced

to 0.1188.

Figure 4-4 Training details when the highest Kappa value was attained, with the

implementation of number 5 data augmentation

In conclusion, the best configuration of dataset for this project would include data pre-

processing with image enhancing method of Gaussian Blur with sigmaX = 10 and data

augmentation combination of number 5 from Table 4-2.




4.3.2 Post-training evaluation

The statistics of the training were created and evaluated by using the best dataset

configuration. Figure 4-5, Figure 4-6 and Figure 4-7 illustrated the line graphs

regarding to the loss and validation loss in graph of mean square error (MSE) of loss

versus epoch, accuracy and validation accuracy in graph of accuracy versus epoch and

Kappa score in graph accuracy versus epoch respectively.

Figure 4-5 Line graph of mean square error (MSE) of loss versus epoch

Figure 4-6 Line graph of accuracy versus epoch in terms of accuracy and validation

accuracy




Figure 4-7 Line graph of accuracy versus epoch in terms of kappa score

From the statistics, the loss was decreasing while accuracy was increasing. This showed

that the model was trying to improve over epochs during training, which indicated a

good sign. Yet, there were fluctuations in validation loss, validation accuracy and

Kappa value over the epochs. In fact, this was a sign of overfitting. This indicated the

model not only learnt the details but also the noises as well, which would negatively

affect the performance of the model. In order to avoid overfitting, reportedly increasing

the batch size to 64 could help in stabilising the fluctuations. However, this project

unable to conduct that due to limitation of GPU memory.




4.3.3 Prediction testing

The model that trained with the best dataset configuration was loaded and performed

severity labels prediction on the APTOS’s test dataset. Since the project had converted

the labels from single labels to multi-labels, the progression of the predicted severity

levels could be observed. Therefore, the progressions of an image were illustrated into

a bar graph in Figure 4-8.

Figure 4-8 The progression of each severity level

From Figure 4-8, the image would be considered as moderate severity instead of severe

or proliferative diabetic retinopathy. This was due to the fact that the threshold of

prediction was set at greater than 0.5. Therefore, the levels that were not greater than

0.5 would not be counted in. At this point, the threshold would not be adjusted.

Figure 4-9 showed the of the format of prediction results in the outputted CSV.

Figure 4-9 The format of prediction results in CSV

On another hand, the prediction on the test dataset was sorted into 2 types, which were

prediction without TTA and prediction with TTA in order to investigate the results

differences and determine which method performed the best.




• Prediction without TTA

The number of images versus each severity label were illustrated in Figure 4-10 as

bar graph. From Figure 4-10, the number of images with severity labels were ranked

as below, from highest to lowest number:

1. Level 2

2. Level 1

3. Level 0

4. Level 3

5. Level 4

Level 2 was drastically more images compared to other levels while level 4 had the

least.

Figure 4-10 Bar graph of the number of images versus the severity labels of

prediction without TTA




• Prediction with TTA

Figure 4-11 showed the bar graph presentation of the number of images versus the

severity labels. From Figure 4-11, the number of images with severity labels were

ranked as below, from highest to lowest number:

1. Level 2

2. Level 3

3. Level 0

4. Level 1

5. Level 4

Similar result with prediction without TTA, Level 2 was significantly more images

compared to other levels while level 4 had the least. On the contrary, prediction

with and without TTA had different opinions on level 0, 1, 3, which were the

middle-class severity levels.

Figure 4-11 Bar graph of the number of images versus the severity labels of

prediction with TTA

In addition, Figure 4-12 and Figure 4-13 showed the test images from each predicted

label from prediction without TTA and prediction with TTA respectively.




Figure 4-12 Test images from each predicted label from prediction without TTA

Figure 4-13 Test images from each predicted label from prediction with TTA

However, verification on the correctness of the predicted labels was unable to be

conducted due to the fact of unavailability of the actual labels on the test dataset from

APTOS, eventually the comparison between predicted and actual labels could not be

done. Therefore, Messidor-2 had become the crucial test dataset for the verification of

the prediction outcomes.

In fact, a competitor of the Kaggle’s competition achieved the quadratic weighted kappa

score of 0.9265, which resulted in the final prediction accuracy of 74% without TTA.

Theoretically, the project attained quadratic weighted kappa score at 0.9308 would

result in the final prediction accuracy that was estimated to be higher than 74% without

TTA.




4.3.4 Verification of prediction results

Similar to prediction testing, the verification of the prediction results also included the

severity labels progression, prediction with and without TTA and used the same model

as in prediction testing. The difference of this section was that the test dataset was

switched to Messidor-2, which made the verification of the validity of the prediction

results could be done since the actual labels of the dataset were available. On top of

that, the test dataset from Messidor-2 could simulate real world prediction since the

model was trained with APTOS train dataset, which from a different source. This was

due to the fact that real world situations would have many images from various of

sources.

In this section, the prediction threshold was tuned from the range of 0.5 to 0.3 in the

prediction with and without TTA in order to obtain the threshold that attained the

highest overall accuracy possible. The reason of the selection of the range was because

the ranges were ideal in avoiding prediction sensitivities that were too low or too high,

which would reduce the overall accuracy drastically.

Figure 4-14 showed the of the format of prediction results in the outputted CSV during

prediction of Messidor-2 dataset whereas Table 4-3 listed the overall accuracy of each

threshold range in prediction with and without TTA.

Figure 4-14 The format of prediction results in CSV (Messidor-2)

Threshold

0.5 0.4 0.3

Without TTA 0.63 0.64 0.64

With TTA 0.63 0.64 0.65

Table 4-3 Overall accuracy of each threshold range in prediction with and without

TTA

From table 4-3, the prediction with TTA with the threshold of 0.3 achieved the highest

overall accuracy, which was 0.65 and this particular setup was configured for further




analysis. On another hand, Table 4-3 also showed that TTA had the inconsistent effect

on improving the overall accuracy by slight margin.

Figure 4-15 showed the bar graph presentation of the number of images versus the

severity labels from the predicted results of Messidor-2. From Figure 4-15, the number

of images with severity labels were ranked as below, from highest to lowest number:

1. Level 0

2. Level 2

3. Level 1

4. Level 3

5. Level 4

Figure 4-15 Bar graph of the number of images versus the severity labels from the

predicted results of Messidor-2




The Figure 4-15 illustrated that level 0 had a lot more images than other levels, which

made it the majority. Figure 4-16 showed the bar graph presentation of the number of

images versus the severity labels from the actual results of Messidor-2. From Figure 4-

16, the number of images with severity labels were ranked as below, from highest to

lowest number:

1. Level 0

2. Level 2

3. Level 1

4. Level 3

5. Level 4

Figure 4-16 Bar graph of the number of images versus the severity labels from the

actual results of Messidor-2

Similarly, the level 0 of actual results was also the majority despite the actual result of

level 0 had much lesser images compared to predicted result. This showed that the

predicted results were more towards level 0. In general, both results were similar in

terms of proportion.

Furthermore, a confusion matrix of actual and predicted results was constructed in

Figure 4-17.




Figure 4-17 Confusion matrix of actual and predicted results

In Figure 4-17, there were 5 images had the actual severity labels of level 0 but

predicted as severity labels of level 4, as highlighted in red colour circle. This was

extremely dangerous in real world diagnosis because the level difference was too far

and it would provide a false positive diagnosis on patients.

Moreover, Figure 4-18 showed the information of the accuracy of the model on

Messidor-2 test dataset.




Figure 4-18 Information of the accuracy of the model on Messidor-2 test dataset

F1-score (highlighted in green colour rectangle) would be the reference on the overall

accuracy of the prediction. From the f1-score values, only accuracy of level 0 was on

par, scored by 0.80. Whereas the other levels were rather low in f1-score. Ultimately,

the overall accuracy achieved was 0.65 or 65%, which was considered as moderately

accurate. Figure 4-19 showed the images from Messidor-2 test dataset that

corresponded with each predicted severity label.

Figure 4-19 Images from Messidor-2 test dataset on each predicted severity label

CHAPTER 5: CONCLUSION



Chapter 5: Conclusion

5.1 Project review

In general, the project involved several phases that were crucial in development in order

to achieve the prediction and classification of the prediction labels.

In project pre-development phase, datasets from APTOS and Messidor-2 that contained

fundus retinal images and image labels were sourced and explored. On top of that, the

suitable model architecture was evaluated and selected for the training of the APTOS

train dataset, which was DenseNet, a CNN-based network architecture.

Moreover, in data pre-processing phase, the images of the datasets were processed with

dark images finding, borders cropping, resizing and most importantly applying each

image enhancement method for trial and error in finding the quadratic weighted kappa

value of each pre-processed dataset in data training.

Furthermore, in model training architecture building and data training phase, the labels

were converted to multi-labels. Next, train/test data splitting technique was utilized for

data validation and the model architecture was built. Before data training, the images

also went through data augmentation process to transform the images in order to reduce

overfitting.

In the last phase, which was prediction on test dataset, the model was involved in

prediction testing on APTOS and Messidor-2 test datasets to evaluate the accuracy of

the prediction model with and without TTA and results were analysed.

Ultimately, a prediction model that could prediction the severity of each fundus retinal

image was built. In terms of validity of prediction results, the highest quadratic

weighted kappa achieved was 0.9308 that resulted in prediction accuracy of higher than

74% (estimated) without TTA on APTOS test dataset and 65% on Messidor-2 dataset.




5.2 Problems encountered

There were some problems encountered during the development of this project.

1. Resource limitations

Goolge Colab had rather limited RAM and GPU memory. This caused some

restrictions during the fine-tuning of hyperparameters such as batch size as well as

images resolution used. Consequently, the prediction model was unable to be

improved further despite there was potential to be refined. Besides, it also caused

the training process extremely time consuming and reduced the efficiency of project

development due to the fact that insufficient computation power by Google Colab.

The training process that involved 50 epochs took about 3 hours to complete.

Although Google Colab offered integration of Google Cloud resources, but the

subscription fee was rather expensive.

2. Insufficient training dataset

In real world, there were many fundus retinal images from different sources, larger

dataset would contribute better data training and prediction performance. In this

project, the size of dataset used was considered relatively small and insufficient to

build a prediction model that was deployable in actual medical application. Even if

there were sufficient datasets available, the computational resources must be

upgraded in the first place in order to ensure it was capable to train the datasets.

3. Unstandardized severity labels that affected prediction accuracy

The accuracy of the prediction of Messidor-2 test dataset was considered as

moderately accurate and had lower accuracy compared to the prediction testing on

APTOS test. This was due to the unstandardized severity labels between APTOS

train dataset and Messidor-2 test dataset. This was because both datasets were from

different sources, which meant that the datasets were graded by different

ophthalmologists who had different opinions on labelling the severity levels. For

instance, when APTOS train dataset labelled an image as level 2 severity but

Messidor-2 dataset had labelled the similar image as level 1 during prediction

testing. In this scenario, the prediction result would be judged as incorrect

prediction and eventually affected the overall accuracy of the model. Such




inconsistency was reflected in Figure 5-1 that showed ophthalmologists were

inconsistent on the judgements on severity labels of a batch of images.

Figure 5-1 The inconsistency of ophthalmologists on the judgements on severity

labels of a batch of images

On another hand, the accuracy result of the prediction model was also due to multi-

class prediction instead of binary prediction. For instance, the prediction would be

easier when the prediction options were only true or false. On the contrary, when

the prediction had a range of prediction options that scale from 0 to 4, it would be

rather challenging for the model to choose the correct option due to lower

probability of being the correct prediction compared to binary prediction.

Eventually, such challenge had largely affected the overall accuracy of the

prediction.




5.3 Future work

In order to further improve the accuracy of the prediction model, some improvements

and hyperparameters fine-tuning could be made, which were:

• Use larger dataset

• Discover alternative image pre-processing methods

• Discover alternative data augmentation combinations

• Switch model architecture and activation function

• Switch loss function

• Switch optimizer

• Find a more suitable learning rate

• Use larger batch size

• Use larger image resolution

• Switch to cross validation

• Adopt transfer learning technique




5.4 Conclusion

Diabetic retinopathy was one of the most common diseases among Malaysians due to

the increasing of diabetic patients. Due to the advancing of A.I, deep learning was

widely used in image-based prediction in medical industry, especially eye disease

prediction. This was because deep learning was a technique that able to automate the

learning process on the data and conduct prediction on similar cases.

In this project, deep learning was applied in the prediction and classification of severity

labels of diabetic retinopathy from fundus retinal images. In order to achieve this, a

CNN-based architecture named DenseNet was built to train the datasets and generated

a prediction model that was capable in predicting the severities of test images. Besides,

various pre-processing methods were also tested in order to enhance the contrast of

images. On top of that, data augmentation and TTA were also utilised and tested in the

development with the purpose of observing the effects of these techniques on the

prediction accuracy.

Ultimately, the prediction results were validated and turned out the prediction model’s

highest quadratic weighted kappa score of 0.9308, which led to the final accuracy of

higher than 74% (estimated) without TTA and 65% on APTOS and Messidor-2 test

datasets respectively. Apparently, the prediction model still had huge room for

improvement.

In conclusion, the development of this project required in-depth deep learning

knowledge and much researches and patience on deciding the hyperparameters and

techniques to be used. Besides, this project provided an exposure or insight on how

deep learning could help in predicting the severities by learning from the fundus retinal

images as well as the encountered problems including the unstandardized severity

labels, which would be useful for relevant research and development in the future.

BIBLIOGRAPHY



BIBLIOGRAPHY

B, N., 2017. Image Data Pre-Processing for Neural Networks. [Online]

Available at: https://becominghuman.ai/image-data-pre-processing-for-neural-

networks-498289068258

[Accessed 10 June 2020].

Bandara, A. M. R. R. & Giragama, P. W. G. R. M. P. B., 2017. A Retinal Image

Enhancement Technique for Blood. IEEE International Conference on

Industrial and Information Systems , p. 6.

Brownlee, J., 2019. Machine Learning Evaluation Metrics in R. [Online]

Available at: https://machinelearningmastery.com/machine-learning-

evaluation-metrics-in-r/

[Accessed 2 August 2020].

Bt. Ngah, N. F. et al., 2017. DIABETIC RETINOPATHY SCREENING. 2nd ed.

Ampang: Ministry of Health Malaysia.

Boyd, K., 2019. What Is Diabetic Retinopathy?. [Online]

Available at: https://www.aao.org/eye-health/diseases/what-is-diabetic-

retinopathy

[Accessed 30 January 2020].

Brush, K., Burns, E. & Rouse, M., 2016. deep learning. [Online]

Available at: https://searchenterpriseai.techtarget.com/definition/deep-

learning-deep-neural-network

[Accessed 2 February 2020].

Chablani, M., 2017. DenseNet. [Online]

Available at: https://towardsdatascience.com/densenet-2810936aeebb

[Accessed 21 July 2020].

BIBLIOGRAPHY



Draelos, R., 2019. Multi-label vs. Multi-class Classification: Sigmoid vs. Softmax.

[Online]

Available at: https://glassboxmedicine.com/2019/05/26/classification-sigmoid-

vs-softmax/


Huang, G., Liu, Z., Maaten, L. v. d. & Weinberger, K. Q., 2018. Densely Connected

Convolutional Networks. p. 9.

Lam, C., Yi, D., Guo, M. & Lindsey, T., 2018. Automated Detection of Diabetic

Retinopathy using Deep Learning. AMIA Joint Summits, Volume 2018, p. 9.

Olaf, R., Fischer, P. & Brox, T., 2015. U-Net: Convolutional Networks for Biomedical

Image Segmentation. Computer Vision and Pattern Recognition, p. 8.

Quelleca, G. et al., 2017. Deep Image Mining for Diabetic Retinopathy Screening.

Medical Image Analysis, Volume 39, p. 19.

Rakin, E., 2018. Malaysia has the highest rate of diabetes in Asia – doctors have

classified the disease as another ‘silent killer. [Online]

Available at: https://www.businessinsider.my/malaysia-highest-rate-diabetes-

silent-killer-asia

[Accessed 25 January 2020].

Reyes, K., 2020. What is Deep Learning and How Does Deep Learning Work. [Online]

Available at: https://www.simplilearn.com/what-is-deep-learning-article

[Accessed 2 February 2020].

Sisodia, D. S., Nair, S. & Khobragade, P., 2017. Diabetic Retinal Fundus Images:

Preprocessing and Feature Extraction for Early Detection of Diabetic

Retinopathy. [Online]

Available at: https://biomedpharmajournal.org/vol10no2/diabetic-retinal-

fundus-images-preprocessing-and-feature-extraction-for-early-detection-of-

diabetic-retinopathy/


BIBLIOGRAPHY



Tsang, S.-H., 2018. Review: DenseNet — Dense Convolutional Network (Image

Classification). [Online]

Available at: https://towardsdatascience.com/review-densenet-image-

classification-b6631a8ef803


Vanmathi, P. & D.Devarajan, 2017. Color Retinal Image Enhancement Based on

Luminosity. Middle-East Journal of Scientific Research, p. 11.

APPENDIX A: POSTER

BCS (Hons) Computer Science A-1


APPENDIX A: POSTER

APPENDIX B: PLAGIARISM CHECK RESULT

BCS (Hons) Computer Science B-1




BCS (Hons) Computer Science B-2


Universiti Tunku Abdul Rahman Form Title : Supervisor’s Comments on Originality Report Generated by Turnitin for Submission of Final Year Project Report (for Undergraduate Programmes) Form Number: FM-IAD-005 Rev No.: 0 Effective Date: 01/10/2013 Page No.: 1of 1

FACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY

Full Name(s) of Candidate(s)

ID Number(s)

Programme / Course

Title of Final Year Project

Similarity Supervisor’s Comments

(Compulsory if parameters of originality exceeds the limits approved by UTAR)

Overall similarity index: %

Similarity by source Internet Sources: % Publications: % Student Papers: %

Number of individual sources listed of more than 3% similarity:

Parameters of originality required and limits approved by UTAR are as follows: (i) Overall similarity index is 20% and below, and (ii) Matching of individual sources listed must be less than 3% each, and (iii) Matching texts in continuous block must not exceed 8 words

Note: Parameters (i) – (ii) shall exclude quotes, bibliography and text matches which are less than 8 words.

Note Supervisor/Candidate(s) is/are required to provide softcopy of full set of the originality report to Faculty/Institute

Based on the above results, I hereby declare that I am satisfied with the originality of the Final Year Project Report submitted by my student(s) as named above.

Signature of Supervisor Signature of Co-Supervisor Name: Name:

Date: Date:

Hoe Yean Sam

16ACB04891

CS

Preliminary Study of Diabetic Retinopathy Classification fromFundus Images using Deep Learning Model

8

5

54

-

Sayed Ahmad Zikri Bin Sayed Aluwee

5 September 2020

APPENDIX C: FYP 2 CHECKLIST

BCS (Hons) Computer Science C-1


UNIVERSITI TUNKU ABDUL RAHMAN

FACULTY OF INFORMATION & COMMUNICATION TECHNOLOGY (KAMPAR CAMPUS)

CHECKLIST FOR FYP2 THESIS SUBMISSION

Student Id

Student Name

Supervisor Name

TICK (√) DOCUMENT ITEMS Your report must include all the items below. Put a tick on the left column after you have

checked your report with respect to the corresponding item. Front Cover

Signed Report Status Declaration Form

Title Page

Signed form of the Declaration of Originality

Acknowledgement

Abstract

Table of Contents

List of Figures (if applicable)

List of Tables (if applicable)

List of Symbols (if applicable)

List of Abbreviations (if applicable)

Chapters / Content

Bibliography (or References)

All references in bibliography are cited in the thesis, especially in the chapter of literature review

Appendices (if applicable)

Poster

Signed Turnitin Report (Plagiarism Check Result - Form Number: FM-IAD-005)

*Include this form (checklist) in the thesis (Bind together as the last page)

I, the author, have checked and confirmed all the items listed in the table are included in my report. ______________________ (Signature of Student) Date:

Supervisor verification. Report with incorrect format can get 5 mark (1 grade) reduction. ______________________ (Signature of Supervisor) Date:

16ACB04891

Hoe Yean Sam

Dr Sayed Ahmad Zikri Bin Sayed Aluwee

5/9/2020 5 September 2020

Documents

PRELIMINARY STUDY OF DIABETIC RETINOPATHY …eprints.utar.edu.my/3952/1/16ACB04891_FYP.pdf2.2.1 Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment with Image