Research ArticleMachine Learning for Brain Images Classification of TwoLanguage Speakers

Alejandro-Israel Barranco-Gutierrez

Catedras CONACyTmdashTecNM Celaya Celaya 38010 Mexico

Correspondence should be addressed to Alejandro-Israel Barranco-Gutierrez israelbarrancoitcelayaedumx

Received 21 December 2019 Revised 11 February 2020 Accepted 20 February 2020 Published 6 June 2020

Guest Editor Horacio Rostro-Gonzalez

Copyright copy 2020 Alejandro-Israel Barranco-Gutierrez is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

e image analysis of the brain with machine learning continues to be a relevant work for the detection of different characteristicsof this complex organ Recent research has observed that there are differences in the structure of the brain specifically in whitematter when learning and using a second language is work focuses on knowing the brain from the classification of MagneticResonance Images (MRIs) of bilingual and monolingual people who have English as their common language Different artificialneural networks of a hidden layer were tested until reaching two neurons in that layer e number of entries used was ninehundred and the classifier registered a high percentage of effectiveness e training was supervised which could be improved in afuture investigation is task is usually carried out by an expert human with Tract-Based Spatial Statistics analysis and fractionalanisotropy expressed in different colors on a screen So this proposal presents another option to quantitatively analyse this type ofphenomena which allows to contribute to neuroscience by automatically detecting bilingual people of monolinguals by usingmachine learning from MRIs is reinforces what is reported in manual detections and the way that a machine can do it

1 Introduction

e use of Magnetic Resonance Imaging (MRI) has reacheda high degree of sophistication because it is useful foranalysing the brain and detecting its diseases however thestructure quantification and tissues has not yet been com-pletely solved [1 2] Several machine learning (ML) tech-niques have been implemented to classify brain activitydiseases and behaviours and have achieved approximatesolutions from this discipline [3ndash6] Even due to the re-markable results of these techniques dedicated hardware iscurrently being created for ML tasks [7ndash9] An interestingexample of ML applied to MRIs is presented by [10] in theirstudy they investigated deep learning framework algorithmsfor predicting the Soil Organic Matter (SOM) content byVIS-NIR spectroscopy Based on fractional-order derivative(15) spectral variation they compared BackpropagationNeural Network (BPN) Multilayer Perceptron (MLP) andConvolutional Neural Network (CNN) including LeNet5and DenseNet10 with full-spectrum data (203 variables) and

a subset of 67 variables highly correlated with the SOMcontent (r2 valuesgt 04) eir results indicate that deeplearning methods including the MLP and CNN can be usedto predict the SOM content from VIS-NIR soil spectra eachdisplaying state-of-the-art performance In [11] a DL modelbased on a CNN is proposed to classify different brain tumortypes using two publicly available datasets e former oneclassifies tumors into (meningioma glioma and pituitarytumor) e other one differentiates between the threeglioma grades (Grade II Grade III and Grade IV) edatasets include 233 and 73 patients with a total of 3064 and516 images on T1-weighted contrast-enhanced images forthe first and second datasets respectively e proposednetwork structure achieves a significant performance eresults indicate the ability of the model for brain tumormulticlassification purposes e authors of [12] propose anovel framework for the classification of fetal brain at anearly age (before the fetus is born) is is a study to classifyfetusesrsquo brain abnormalities of widespread Gestational Ages(GAs) e study incorporates several machine learning

HindawiComputational Intelligence and NeuroscienceVolume 2020 Article ID 9045456 7 pageshttpsdoiorg10115520209045456

classifiers such as Diagonal Quadratic DiscriminatesAnalysis (DQDA) K-Nearest Neighbour (K-NN) randomforest naıve Bayes and Radial Basis Function (RBF) neuralnetwork classifiers Moreover several bagging and Ada-boosting ensemble models have been constructed usingrandom forest naıve Bayes and RBF network classifierseperformances of these ensembles have been compared withtheir individual models eirs results show that the ap-proach can successfully identify and classify numerous typesof defects within MRI images of the fetal brain of variousGAs Using the KNN classifier it is able to achieve thehighest classification accuracy In [13] the authors presentan intuitionistic fuzzy kernel clustering (IIFKC) methodusing intuitionistic fuzzy set theory that encompasses akernel-based distance function e proposed methodpreserves the image information insensitive to noise andfree of prerequisites of fine-tuning parameters e seg-mentation results attained on the real and simulated MRIbrain image exhibits the efficiency of the IIFKC method andenhances the performance in comparison with the existingmethods in terms of similarity index Jaccard coefficientsand execution time Varuna-Shree and Kumar [14] present anoise removal technique extraction of Gray-Level Co-oc-currence Matrix (GLCM) features and Discrete WaveletTransformation- (DWT-) based brain tumor region growingsegmentation to reduce the complexity and improve theperformance is was followed by morphological filteringwhich removes the noise that can be formed after seg-mentation e probabilistic neural network classifier wasused to train and test the performance accuracy in thedetection of tumor location in brain MRI images In [15] astudy of Berkeley Wavelet Transformation- (BWT-) basedbrain tumor segmentation is shown Furthermore to im-prove the accuracy and quality rate of the Support VectorMachine- (SVM-) based classifier relevant features areextracted from each segmented tissue e experimentalresults of proposed technique have been evaluated and val-idated for performance and quality analysis on magneticresonance brain images based on accuracy sensitivityspecificity and dice similarity index coefficient e experi-mental effectiveness of the proposed technique for identifyingnormal and abnormal tissues from brain MRIs e researchreported in [16] proposes a Probabilistic Neural Network-Radial Basis Function method to increase the classificationaccuracy in tumor functional brain images e classificationis carried out by extracting the features by using multilevelwavelet method en the morphological filtering techniqueis used in segmentation process where the Region of Interest(ROI) areas of the brain functional images are compared withthe neighbourhood pixels is technique yields a high ac-curacy for the functional brain images is procedure isefficient to diagnose the tumor region in early stage itself

On the contrary current research studies indicate thatlearning and using a Second Language (L2) can affect thestructure of the brain White Matter (WM) tracts and GrayMatter (GM) tracts [17ndash19] is observation comes fromresearch studies that analyse early and older bilingual peoplewho have been using their first and second languages forseveral years [20ndash22] is change caused by L2 is presumed

positive because lifelong bilingualism contributes to thecognitive reserve against decreasing the integrity of whitematter in aging [23 24] Sulpizio et al affirm that ldquocurrentlyno agreement on factor modulates most effectively and en-duringly brain plasticity in bilingual individualsrdquo [25] eyconsider that the classification of bilingualism versusmonolingualism can hide detailed changes in neuronsderived from experience in language practice thus leading tovariable and often conflicting findings And they present theeffect of the Age of Acquisition (AoA) ese findings shednew light on the importance of modelling bilingualism as agradient measure rather than an all-or-none phenomenon

In this work we analysed the literature regarding theclassification of brain images withmachine learning techniquesfrom magnetic resonance imaging is is to review thetechniques that have been used for classification and to observethe importance of the sensor tomeasure the variable that needsto be analysed as well as the preprocessing necessary to feed theclassification systemis is in order to help the classifier in histask In this case it has the hypothesis that you can classifybrain images by examining the corpus callosum bilaterallyincluding the genu the body and the previous part of thesplenium from MRIs In order to prove it the work was or-ganized as follows In Section 2 the characteristics of thedatabase as well as the process to obtain the variation of datathat feeds information to the neural network are explainedSection 3 presents the experimental outcomes of the classifi-cation using different architectures of neural networks Section4 presents the authorrsquos interpretation of the results Finally theconclusions and future works are presented

2 Materials and Methods

21 Database e raw material for this work is the L2strucdatabase hosted in XNAT Central freely available onlinethrough the PNAS open access option e share data wasapproved by the University of Reading Research EthicsCommittee All participants provided written informedconsent prior to participating [17] It was used a 30-TeslaSiemens MAGNETOM Trio MRI scanner with Syngo soft-ware and 36-channel Head Matrix coil to acquire a whole-brain diffusion-weighted Echo-Plannar Imaging image (twoaverages 30 directions 60 axial slices slice thickness 2mmno interslice gap field of view 256times 256mm acquisitionmatrix 128times128 voxel size 2mm isotropic echo time93ms repetition time 8200ms b-value 1000 smm2) A 3Dbrain shot participant number 101 is presented in Figure 1 Aset of 60 cross sections can be observed at different heights ofthe brain arranged in rows from 1 to 8 and in columns fromA to H For the implementation of the method Matlab 2019and its ldquodicomreadrdquo function were used to manipulate theRMI files while the implementation of the Artificial NeuralNetwork (ANN) was carried out withMatlabrsquos ldquonprtoolrdquo tool

22 Data Variance e cross-section variance for eachvoxel was performed to locate the areas where major changesexist in the same participant on different tomography As anexample in Figure 2 it has the level variance of 62

2 Computational Intelligence and Neuroscience

tomography of the same brain slice in order to observe whatare the interest areas regardless of whether they speak one ortwo languages It can see more changes in the frontal andoccipital lobes And the same happens with the variancespresented in Figure 3

e same procedure was performed for a Native Speaker(NS) participant e result is shown in Figure 3 where itcan be seen that the corpus callosum has higher variancelevels in NS participant than L2 participant Both NS and L2participants are male with PhD studies

e literature indicates that the differences betweenbilingual and monolingual are reflected in the corpus cal-losum bilaterally this is why that brain zone was selected toclassify between volunteers L2 and NS Also the 30 by 30matrix form is to facilitate data acquisition which could befurther optimized Figure 4 shows the analysis slice (D4respect to Figure 1) among the 60 existing for each to-mography of the 62 performed for each volunteeraccording to Figures 2 and 3 together with the analysisperformed in [17]

e 75 of the data were taken for training 10 forvalidation and 15 for testing Due to size defects in theimage of participant 105 it was removed from the experi-ment erefore the tomography of nineteen L2-type par-ticipants and twenty-five NS-type participants was used Toreinforce the training the training dataset was repeated threetimes and this significantly reduced the error e 30 by 30matrix was reshaped in an input vector of 900 elementswhere each element was the normalized mean of 62 voxels ofeach participant Normalization was carried out with respectto the maximum of each participant

xij xij

max xtij1113872 1113873

(1)

where i and j are the row and column indices respectivelyand t is the tomography index

23 Machine Learning In order to measure the ease orcomplexity with which a machine learning system canclassify between L2 and NS speakers MRIs different neural

1

2

3

4

5

6

7

8

A B C D E F G H

Figure 1 Set of 60 images of cross sections of the brain of the volunteer 101

45004000350030002500200015001000

5000

020

40

6080

100

0204060

Cross-section variance of 2860 image of the volunteer 101 type L2

80100120

Figure 2 Set of 62 cross-section variance of brain images for the volunteer type L2

Computational Intelligence and Neuroscience 3

network architectures were tested until it reached a simpleperceptron and one of them is shown in Figure 5 e testswere carried out using single hidden layer neural networksvarying its number of neuronse feedforward architectureof the neural networks used is shown as follows

Output log sig W2lowast tan sig IlowastW1 + b1( 1113857 + b2( 1113857 (2)

where I is the inputs vector W1 is the weights vector frominputs to the hidden layer b1 the first layer bias vector W2 isthe weights vector from the hidden layer to the output b2 theoutput layer bias vector tan sig is the hyperbolic tangentsigmoid transfer function and log sig is the log-sigmoidtransfer function

e experiment used the nprtool and this data classi-fication library only needs the input data the targets in theoutput and the number of neurons in the hidden layer to

train a neural network e pattern recognition network inMATLAB uses the default Scaled Conjugate GradientBackpropagation algorithm for training e data wererandomly divided into 92 20 and 20 samples for trainingvalidation and testing respectively

3 Results

In order to measure the efficiency in the L2 and NS par-ticipant detection using different quantity of neurons in thehidden layer three representative tests were realized andreported in this section erefore the error vs epochs usedfor the training validation and test performances wereplotted And also their Receiver Operating Characteristic(ROC) curves for training validation testing and threetogether were constructed which serve to evaluate the

3000

2500

2000

1500

1000

500

0

020

4060

80100

0204060

Cross-section variance of 2860 image of the volunteer 207 type NS

80100120

Figure 3 Set of 62 cross-section variance of brain images for the volunteer type NS

Analysis slice

(a)

Analysis zone

(b)

Figure 4 Selected data for the classification of brain images of only English speakers (NS) or two languages (L2) (a) Analysis slice and (b)slice zone

4 Computational Intelligence and Neuroscience

classifiers performance In the ROC curves it is possible tosee which of the curves is nearby to the maximum value ofthe vertical axis (True Positive Rate) and similarly for thehorizontal axis (False Positive Rate) Mathematically it canbe said that the classifier with more area under the ROCcurve has the best efficiency

e first experiment used one hundred neurons its errorfor each epoch is shown in Figure 6(a) and its ROC curves inFigure 6(b)

In the same way the second experiment used one twoneurons and its error for each epoch is shown in Figure 7(a)and its ROC curves in Figure 7(b)

e last experiment used one neuron and its error foreach epoch is shown in Figure 8(a) and its ROC curves inFigure 8(b)

4 Discussion

According to the results it can be confirmed from machinelearning what recent literature asserts regarding structuralchanges in the brain was induced by speaking two languagesIn this work it shows that also an ANN finds differences inWM from bilingual people who learn their second language(L2) and are active users of both languages Lately this RMI

analysis is carried out with Tract-Based Spatial Statisticswhich represents a dependence on the talent of the statisticalanalyst In contrast to artificial neural networks the analysiscan be automated which is demonstrated and contributed inthis work and is relatively an easy task for a machinelearning system

With only the central section of the brain it was possibleto classify between L2 andNS participants with a high degreeof efficiency as shown by the ROC curves of Figures 6 and 7is is very important to observe how machine learningtools can help in the analysis of brain image in this case ofthe RMI type And even more it was observed that whenusing the aforementioned RMI scanner the analysis ofspatial statistics based on the tract and fractional anisotropyrequires 128times128times 0 inputs while the red neural uses30times 30times1 inputs to decide if a participant is of type L2 ortype NS which represents a substantial reduction in in-formation processing Regarding the computational com-plexity of the forward neural network it is of type O (n)where n is the number of inputs and the training method isthe classic backpropagation and this is linear in the numberof training samples that is the operation to update theweights for a single sample is constant

InputOutput

Output

900100 1

++1

Hidden

W2W1

b2b1

Figure 5 Architecture of the initial artificial neural network used to classify RMIs of L2 and NS participants

100

10ndash2

10ndash4

10ndash6

10ndash8

0 5 10 15 20 25 30 35 4048 epochs

Cros

sent

ropy

45

Best validation performance is 13534e ndash 07 at epoch 48

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 6 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hundred hidden neurons

Computational Intelligence and Neuroscience 5

5 Conclusions

e study of the brain continues to be very active due to itscomplexity Although there is a broad vision of its rolewithin the body there are still many unknowns to solve Toknowmore about it this work developed a machine learningsystem for brain image classification of two languagespeakers is is very relevant because it opens anotheroption to quantitatively know the brain from machine

learning since the tool used par excellence for this type ofanalysis is the Tract-Based Spatial Statistics

e classification was applied to magnetic resonanceimaging of a group of 19 people who speak English as asecond language and who are 13 to 374 months using it withan average of 105 years of age in the English languageacquisition and also from a second group of 25 participantswho only speak one language and their native language isEnglish For this task different artificial neural networks of a

100

10ndash2

10ndash4

10ndash6

0 10 20 30 40 5068 epochs

Cros

sent

ropy

60

Best validation performance is 37897e ndash 07 at epoch 68

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 7 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using two hidden neurons

101

100

10ndash1

10ndash2

0 5 10 15 20 2525 epochs

Cros

sent

ropy

Best validation performance is 029684 at epoch 19

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04Tr

ue p

ositi

ve ra

te02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 8 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hidden neuron

6 Computational Intelligence and Neuroscience

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7

tomography of the same brain slice in order to observe whatare the interest areas regardless of whether they speak one ortwo languages It can see more changes in the frontal andoccipital lobes And the same happens with the variancespresented in Figure 3

e same procedure was performed for a Native Speaker(NS) participant e result is shown in Figure 3 where itcan be seen that the corpus callosum has higher variancelevels in NS participant than L2 participant Both NS and L2participants are male with PhD studies

e literature indicates that the differences betweenbilingual and monolingual are reflected in the corpus cal-losum bilaterally this is why that brain zone was selected toclassify between volunteers L2 and NS Also the 30 by 30matrix form is to facilitate data acquisition which could befurther optimized Figure 4 shows the analysis slice (D4respect to Figure 1) among the 60 existing for each to-mography of the 62 performed for each volunteeraccording to Figures 2 and 3 together with the analysisperformed in [17]

e 75 of the data were taken for training 10 forvalidation and 15 for testing Due to size defects in theimage of participant 105 it was removed from the experi-ment erefore the tomography of nineteen L2-type par-ticipants and twenty-five NS-type participants was used Toreinforce the training the training dataset was repeated threetimes and this significantly reduced the error e 30 by 30matrix was reshaped in an input vector of 900 elementswhere each element was the normalized mean of 62 voxels ofeach participant Normalization was carried out with respectto the maximum of each participant

xij xij

max xtij1113872 1113873

(1)

where i and j are the row and column indices respectivelyand t is the tomography index

23 Machine Learning In order to measure the ease orcomplexity with which a machine learning system canclassify between L2 and NS speakers MRIs different neural

1

2

3

4

5

6

7

8

A B C D E F G H

Figure 1 Set of 60 images of cross sections of the brain of the volunteer 101

45004000350030002500200015001000

5000

020

40

6080

100

0204060

Cross-section variance of 2860 image of the volunteer 101 type L2

80100120

Figure 2 Set of 62 cross-section variance of brain images for the volunteer type L2

Computational Intelligence and Neuroscience 3

network architectures were tested until it reached a simpleperceptron and one of them is shown in Figure 5 e testswere carried out using single hidden layer neural networksvarying its number of neuronse feedforward architectureof the neural networks used is shown as follows

Output log sig W2lowast tan sig IlowastW1 + b1( 1113857 + b2( 1113857 (2)

where I is the inputs vector W1 is the weights vector frominputs to the hidden layer b1 the first layer bias vector W2 isthe weights vector from the hidden layer to the output b2 theoutput layer bias vector tan sig is the hyperbolic tangentsigmoid transfer function and log sig is the log-sigmoidtransfer function

e experiment used the nprtool and this data classi-fication library only needs the input data the targets in theoutput and the number of neurons in the hidden layer to

train a neural network e pattern recognition network inMATLAB uses the default Scaled Conjugate GradientBackpropagation algorithm for training e data wererandomly divided into 92 20 and 20 samples for trainingvalidation and testing respectively

3 Results

In order to measure the efficiency in the L2 and NS par-ticipant detection using different quantity of neurons in thehidden layer three representative tests were realized andreported in this section erefore the error vs epochs usedfor the training validation and test performances wereplotted And also their Receiver Operating Characteristic(ROC) curves for training validation testing and threetogether were constructed which serve to evaluate the

3000

2500

2000

1500

1000

500

0

020

4060

80100

0204060

Cross-section variance of 2860 image of the volunteer 207 type NS

80100120

Figure 3 Set of 62 cross-section variance of brain images for the volunteer type NS

Analysis slice

(a)

Analysis zone

(b)

Figure 4 Selected data for the classification of brain images of only English speakers (NS) or two languages (L2) (a) Analysis slice and (b)slice zone

4 Computational Intelligence and Neuroscience

classifiers performance In the ROC curves it is possible tosee which of the curves is nearby to the maximum value ofthe vertical axis (True Positive Rate) and similarly for thehorizontal axis (False Positive Rate) Mathematically it canbe said that the classifier with more area under the ROCcurve has the best efficiency

e first experiment used one hundred neurons its errorfor each epoch is shown in Figure 6(a) and its ROC curves inFigure 6(b)

In the same way the second experiment used one twoneurons and its error for each epoch is shown in Figure 7(a)and its ROC curves in Figure 7(b)

e last experiment used one neuron and its error foreach epoch is shown in Figure 8(a) and its ROC curves inFigure 8(b)

4 Discussion

According to the results it can be confirmed from machinelearning what recent literature asserts regarding structuralchanges in the brain was induced by speaking two languagesIn this work it shows that also an ANN finds differences inWM from bilingual people who learn their second language(L2) and are active users of both languages Lately this RMI

analysis is carried out with Tract-Based Spatial Statisticswhich represents a dependence on the talent of the statisticalanalyst In contrast to artificial neural networks the analysiscan be automated which is demonstrated and contributed inthis work and is relatively an easy task for a machinelearning system

With only the central section of the brain it was possibleto classify between L2 andNS participants with a high degreeof efficiency as shown by the ROC curves of Figures 6 and 7is is very important to observe how machine learningtools can help in the analysis of brain image in this case ofthe RMI type And even more it was observed that whenusing the aforementioned RMI scanner the analysis ofspatial statistics based on the tract and fractional anisotropyrequires 128times128times 0 inputs while the red neural uses30times 30times1 inputs to decide if a participant is of type L2 ortype NS which represents a substantial reduction in in-formation processing Regarding the computational com-plexity of the forward neural network it is of type O (n)where n is the number of inputs and the training method isthe classic backpropagation and this is linear in the numberof training samples that is the operation to update theweights for a single sample is constant

InputOutput

Output

900100 1

++1

Hidden

W2W1

b2b1

Figure 5 Architecture of the initial artificial neural network used to classify RMIs of L2 and NS participants

100

10ndash2

10ndash4

10ndash6

10ndash8

0 5 10 15 20 25 30 35 4048 epochs

Cros

sent

ropy

45

Best validation performance is 13534e ndash 07 at epoch 48

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 6 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hundred hidden neurons

Computational Intelligence and Neuroscience 5

5 Conclusions

e study of the brain continues to be very active due to itscomplexity Although there is a broad vision of its rolewithin the body there are still many unknowns to solve Toknowmore about it this work developed a machine learningsystem for brain image classification of two languagespeakers is is very relevant because it opens anotheroption to quantitatively know the brain from machine

learning since the tool used par excellence for this type ofanalysis is the Tract-Based Spatial Statistics

e classification was applied to magnetic resonanceimaging of a group of 19 people who speak English as asecond language and who are 13 to 374 months using it withan average of 105 years of age in the English languageacquisition and also from a second group of 25 participantswho only speak one language and their native language isEnglish For this task different artificial neural networks of a

100

10ndash2

10ndash4

10ndash6

0 10 20 30 40 5068 epochs

Cros

sent

ropy

60

Best validation performance is 37897e ndash 07 at epoch 68

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 7 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using two hidden neurons

101

100

10ndash1

10ndash2

0 5 10 15 20 2525 epochs

Cros

sent

ropy

Best validation performance is 029684 at epoch 19

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04Tr

ue p

ositi

ve ra

te02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 8 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hidden neuron

6 Computational Intelligence and Neuroscience

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7

network architectures were tested until it reached a simpleperceptron and one of them is shown in Figure 5 e testswere carried out using single hidden layer neural networksvarying its number of neuronse feedforward architectureof the neural networks used is shown as follows

Output log sig W2lowast tan sig IlowastW1 + b1( 1113857 + b2( 1113857 (2)

where I is the inputs vector W1 is the weights vector frominputs to the hidden layer b1 the first layer bias vector W2 isthe weights vector from the hidden layer to the output b2 theoutput layer bias vector tan sig is the hyperbolic tangentsigmoid transfer function and log sig is the log-sigmoidtransfer function

e experiment used the nprtool and this data classi-fication library only needs the input data the targets in theoutput and the number of neurons in the hidden layer to

train a neural network e pattern recognition network inMATLAB uses the default Scaled Conjugate GradientBackpropagation algorithm for training e data wererandomly divided into 92 20 and 20 samples for trainingvalidation and testing respectively

3 Results

In order to measure the efficiency in the L2 and NS par-ticipant detection using different quantity of neurons in thehidden layer three representative tests were realized andreported in this section erefore the error vs epochs usedfor the training validation and test performances wereplotted And also their Receiver Operating Characteristic(ROC) curves for training validation testing and threetogether were constructed which serve to evaluate the

3000

2500

2000

1500

1000

500

0

020

4060

80100

0204060

Cross-section variance of 2860 image of the volunteer 207 type NS

80100120

Figure 3 Set of 62 cross-section variance of brain images for the volunteer type NS

Analysis slice

(a)

Analysis zone

(b)

Figure 4 Selected data for the classification of brain images of only English speakers (NS) or two languages (L2) (a) Analysis slice and (b)slice zone

4 Computational Intelligence and Neuroscience

classifiers performance In the ROC curves it is possible tosee which of the curves is nearby to the maximum value ofthe vertical axis (True Positive Rate) and similarly for thehorizontal axis (False Positive Rate) Mathematically it canbe said that the classifier with more area under the ROCcurve has the best efficiency

e first experiment used one hundred neurons its errorfor each epoch is shown in Figure 6(a) and its ROC curves inFigure 6(b)

In the same way the second experiment used one twoneurons and its error for each epoch is shown in Figure 7(a)and its ROC curves in Figure 7(b)

e last experiment used one neuron and its error foreach epoch is shown in Figure 8(a) and its ROC curves inFigure 8(b)

4 Discussion

According to the results it can be confirmed from machinelearning what recent literature asserts regarding structuralchanges in the brain was induced by speaking two languagesIn this work it shows that also an ANN finds differences inWM from bilingual people who learn their second language(L2) and are active users of both languages Lately this RMI

analysis is carried out with Tract-Based Spatial Statisticswhich represents a dependence on the talent of the statisticalanalyst In contrast to artificial neural networks the analysiscan be automated which is demonstrated and contributed inthis work and is relatively an easy task for a machinelearning system

With only the central section of the brain it was possibleto classify between L2 andNS participants with a high degreeof efficiency as shown by the ROC curves of Figures 6 and 7is is very important to observe how machine learningtools can help in the analysis of brain image in this case ofthe RMI type And even more it was observed that whenusing the aforementioned RMI scanner the analysis ofspatial statistics based on the tract and fractional anisotropyrequires 128times128times 0 inputs while the red neural uses30times 30times1 inputs to decide if a participant is of type L2 ortype NS which represents a substantial reduction in in-formation processing Regarding the computational com-plexity of the forward neural network it is of type O (n)where n is the number of inputs and the training method isthe classic backpropagation and this is linear in the numberof training samples that is the operation to update theweights for a single sample is constant

InputOutput

Output

900100 1

++1

Hidden

W2W1

b2b1

Figure 5 Architecture of the initial artificial neural network used to classify RMIs of L2 and NS participants

100

10ndash2

10ndash4

10ndash6

10ndash8

0 5 10 15 20 25 30 35 4048 epochs

Cros

sent

ropy

45

Best validation performance is 13534e ndash 07 at epoch 48

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 6 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hundred hidden neurons

Computational Intelligence and Neuroscience 5

5 Conclusions

e study of the brain continues to be very active due to itscomplexity Although there is a broad vision of its rolewithin the body there are still many unknowns to solve Toknowmore about it this work developed a machine learningsystem for brain image classification of two languagespeakers is is very relevant because it opens anotheroption to quantitatively know the brain from machine

learning since the tool used par excellence for this type ofanalysis is the Tract-Based Spatial Statistics

e classification was applied to magnetic resonanceimaging of a group of 19 people who speak English as asecond language and who are 13 to 374 months using it withan average of 105 years of age in the English languageacquisition and also from a second group of 25 participantswho only speak one language and their native language isEnglish For this task different artificial neural networks of a

100

10ndash2

10ndash4

10ndash6

0 10 20 30 40 5068 epochs

Cros

sent

ropy

60

Best validation performance is 37897e ndash 07 at epoch 68

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 7 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using two hidden neurons

101

100

10ndash1

10ndash2

0 5 10 15 20 2525 epochs

Cros

sent

ropy

Best validation performance is 029684 at epoch 19

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04Tr

ue p

ositi

ve ra

te02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 8 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hidden neuron

6 Computational Intelligence and Neuroscience

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7

classifiers performance In the ROC curves it is possible tosee which of the curves is nearby to the maximum value ofthe vertical axis (True Positive Rate) and similarly for thehorizontal axis (False Positive Rate) Mathematically it canbe said that the classifier with more area under the ROCcurve has the best efficiency

e first experiment used one hundred neurons its errorfor each epoch is shown in Figure 6(a) and its ROC curves inFigure 6(b)

In the same way the second experiment used one twoneurons and its error for each epoch is shown in Figure 7(a)and its ROC curves in Figure 7(b)

e last experiment used one neuron and its error foreach epoch is shown in Figure 8(a) and its ROC curves inFigure 8(b)

4 Discussion

According to the results it can be confirmed from machinelearning what recent literature asserts regarding structuralchanges in the brain was induced by speaking two languagesIn this work it shows that also an ANN finds differences inWM from bilingual people who learn their second language(L2) and are active users of both languages Lately this RMI

analysis is carried out with Tract-Based Spatial Statisticswhich represents a dependence on the talent of the statisticalanalyst In contrast to artificial neural networks the analysiscan be automated which is demonstrated and contributed inthis work and is relatively an easy task for a machinelearning system

With only the central section of the brain it was possibleto classify between L2 andNS participants with a high degreeof efficiency as shown by the ROC curves of Figures 6 and 7is is very important to observe how machine learningtools can help in the analysis of brain image in this case ofthe RMI type And even more it was observed that whenusing the aforementioned RMI scanner the analysis ofspatial statistics based on the tract and fractional anisotropyrequires 128times128times 0 inputs while the red neural uses30times 30times1 inputs to decide if a participant is of type L2 ortype NS which represents a substantial reduction in in-formation processing Regarding the computational com-plexity of the forward neural network it is of type O (n)where n is the number of inputs and the training method isthe classic backpropagation and this is linear in the numberof training samples that is the operation to update theweights for a single sample is constant

InputOutput

Output

900100 1

++1

Hidden

W2W1

b2b1

Figure 5 Architecture of the initial artificial neural network used to classify RMIs of L2 and NS participants

100

10ndash2

10ndash4

10ndash6

10ndash8

0 5 10 15 20 25 30 35 4048 epochs

Cros

sent

ropy

45

Best validation performance is 13534e ndash 07 at epoch 48

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 6 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hundred hidden neurons

Computational Intelligence and Neuroscience 5

5 Conclusions

e study of the brain continues to be very active due to itscomplexity Although there is a broad vision of its rolewithin the body there are still many unknowns to solve Toknowmore about it this work developed a machine learningsystem for brain image classification of two languagespeakers is is very relevant because it opens anotheroption to quantitatively know the brain from machine

learning since the tool used par excellence for this type ofanalysis is the Tract-Based Spatial Statistics

e classification was applied to magnetic resonanceimaging of a group of 19 people who speak English as asecond language and who are 13 to 374 months using it withan average of 105 years of age in the English languageacquisition and also from a second group of 25 participantswho only speak one language and their native language isEnglish For this task different artificial neural networks of a

100

10ndash2

10ndash4

10ndash6

0 10 20 30 40 5068 epochs

Cros

sent

ropy

60

Best validation performance is 37897e ndash 07 at epoch 68

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 7 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using two hidden neurons

101

100

10ndash1

10ndash2

0 5 10 15 20 2525 epochs

Cros

sent

ropy

Best validation performance is 029684 at epoch 19

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04Tr

ue p

ositi

ve ra

te02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 8 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hidden neuron

6 Computational Intelligence and Neuroscience

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7

5 Conclusions

e study of the brain continues to be very active due to itscomplexity Although there is a broad vision of its rolewithin the body there are still many unknowns to solve Toknowmore about it this work developed a machine learningsystem for brain image classification of two languagespeakers is is very relevant because it opens anotheroption to quantitatively know the brain from machine

learning since the tool used par excellence for this type ofanalysis is the Tract-Based Spatial Statistics

e classification was applied to magnetic resonanceimaging of a group of 19 people who speak English as asecond language and who are 13 to 374 months using it withan average of 105 years of age in the English languageacquisition and also from a second group of 25 participantswho only speak one language and their native language isEnglish For this task different artificial neural networks of a

100

10ndash2

10ndash4

10ndash6

0 10 20 30 40 5068 epochs

Cros

sent

ropy

60

Best validation performance is 37897e ndash 07 at epoch 68

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 7 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using two hidden neurons

101

100

10ndash1

10ndash2

0 5 10 15 20 2525 epochs

Cros

sent

ropy

Best validation performance is 029684 at epoch 19

TrainValidation

TestBest

(a)

Training ROC Validation ROC

Test ROC All ROC

1

08

06

04

True

pos

itive

rate

False positive rate

02

01080604020

False positive rate1080604020

False positive rate1080604020

False positive rate1080604020

1

08

06

04Tr

ue p

ositi

ve ra

te02

0

1

08

06

04

True

pos

itive

rate

02

0

1

08

06

04

True

pos

itive

rate

02

0

(b)

Figure 8 (a) e error vs epochs used for the training validation and test performances (b) ROC (Receiver Operating Characteristic)training validation and testing curves using one hidden neuron

6 Computational Intelligence and Neuroscience

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7

hidden layer with 900 inputs were designed It wasexperimented with different amounts of hidden neuronsuntil reaching a neuron and obtaining good results althoughit was chosen to use two neurons to ensure 100 effec-tiveness in the classification of images from the databaseused

e system can be extended with an investigation thatreduces the number of ANN entries by analysing the voxelsthat provide more and better information for classificatione way to describe language learning from the brainstructure in a gradual and quantitative manner could also besought

Data Availability

e RMIs reported in this paper have been downloaded fromXNATCentral httpscentralxnatorg (Project ID code L2struc)e participantsrsquo information is at httpwwwpnasorglookupsuppldoi101073pnas1414183112-DCSupplemental

Conflicts of Interest

e author declares that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is research was granted by CONACYT and TecNM withCatedras CONACYT and Sistem a Nacional de Inves-tigadores programs I thank to Dr Jose Alfredo Padilla DrFrancisco Javier Perez Pinal and Dr Juan Prado Olivares fortheir valuable support

References

[1] S S Tiwari and M Pant ldquoBrain tumor segmentation andclassification from magnetic resonance images review ofselected methods from 2014 to 2019rdquo Pattern RecognitionLetters 2019

[2] P Kanmani and P Marikkann ldquoMRI brain images classifi-cation a multi-level threshold based region optimizationtechniquerdquo Journal of Medical Systems vol 42 no 4 2018

[3] M Madheswaran and D A Sahaya-Dhas ldquoClassification ofbrain MRI images using support vector machine with variouskernelsrdquo Biomedical Research vol 26 no 3 2015

[4] C Da H Zhang and Y Sang ldquoBrain CT image classificationwith deep neural networksrdquo in Proceedings of the 18th AsiaPacific Symposium on Intelligent and Evolutionary Systemsvol 1 Singapore 2015

[5] A E Fetit J Novak A C Peet and T N Arvanitis ldquo3Dtexture analysis of MR images to improve classification ofpaediatric brain tumours a preliminary studyrdquo Studies inHealth Technology and Informatics vol 202 2014

[6] Y D Zhang and L Wu ldquoAn MR brain images classifier viaprincipal component analysis and kernel support vector ma-chinerdquo Progress in Electromagnetics Research vol 130 2012

[7] T Coughlin ldquoAI inference and storagerdquo IEEE ConsumerElectronics Magazine vol 9 no 1 2020

[8] M Arredondo-Velazquez J Diaz-Carmona C Torres-Huitzil A-I Barranco-Gutierrez A Padilla-Medina andJ Prado-Olivarez ldquoA streaming accelerator of convolutional

neural networks for resource-limited applicationsrdquo Elec-tronics Express vol 16 no 23 2019

[9] M J Villasentildeor-Aguilar J E Botello-Alvarez F J Perez-Pinalet al ldquoFuzzy classification of the maturity of the tomato usinga vision systemrdquo Journal of Sensor vol 2019 Article ID3175848 12 pages 2019

[10] Z Xu X Zhao X Guo and J Guo ldquoDeep learning appli-cation for predicting soil organic matter content by VIS-NIRspectroscopyrdquo Computational Intelligence and Neurosciencevol 2019 Article ID 3563761 11 pages 2019

[11] H H Sultan N M Salem and W Al-Atabany ldquoMulti-classification of brain tumor images using deep neural net-workrdquo IEEE Access vol 7 2019

[12] O Attallah M A Sharkas and H Gadelkarim ldquoFetal brainabnormality classification from MRI images of differentgestational agerdquo Brain Sciences vol 9 no 9 2019

[13] S Saladi and A Prabha-Nagarajan ldquoA novel fuzzy factor forMRI brain image segmentation using intuitionistic fuzzykernel clustering approachrdquo Journal of Advanced Research inDynamical and Control Systems vol 10 2018

[14] N Varuna-Shree and T N R Kumar ldquoIdentification andclassification of brain tumor MRI images with feature ex-traction using DWTand probabilistic neural networkrdquo BrainInformatics vol 5 no 1 2018

[15] N Bhaskarrao-Bahadure A Kumar-Ray and H Pal-ethildquoImage analysis for MRI based brain tumor detectionand feature extraction using biologically inspired BWT andSVMrdquo International Journal of Biomedical Imaging vol 2017Article ID 9749108 12 pages 2017

[16] A Veeramuthu S Meenakshi and V Priya Darsini ldquoBrainimage classification using learning machine approach and brainstructure analysisrdquo Procedia Computer Science vol 50 2015

[17] C Pliatsikasa E Moschopoulouc and J D Saddy ldquoe effectsof bilingualism on the white matter structure of the brainrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 112 no 5 2015

[18] P Pliatsikas T Johnstone and T Marinis ldquoGrey mattervolume in the cerebellum is related to the processing ofgrammatical rules in a second language a structural voxel-based morphometry studyrdquo Cerebellum vol 13 no 1 2014

[19] J Abutalebi andDW Green ldquoBilingual language productionthe neurocognition of language representation and controlrdquoJournal of Neurolinguistics vol 20 no 3 2007

[20] G Bubbico P Chiacchiaretta M Parenti et al ldquoEffects ofsecond language learning on the plastic aging brain func-tional connectivity cognitive decline and reorganizationrdquoFrontiers in Neuroscience vol 13 2019

[21] P Li J Legault and K A Litcofsky ldquoNeuroplasticity as afunction of second language learning anatomical changes inthe human brainrdquo Cortex vol 58 2014

[22] M Stein C Winkler A Kaiser and T Dierks ldquoStructuralbrain changes related to bilingualism does immersion make adifferencerdquo Frontiers in Psychology vol 5 2014

[23] B T Gold N F Johnson and D K Powell ldquoLifelong bi-lingualism contributes to cognitive reserve against whitematter integrity declines in agingrdquo Neuropsychologia vol 51no 13 2013

[24] G Luk E Bialystok F I Craik and C L Grady ldquoLifelongbilingualismmaintains white matter integrity in older adultsrdquoJournal of Neuroscience vol 31 no 46 2011

[25] S Sulpizio N Del-Maschio G Del-Mauro D Fedeli andJ Abutalebi ldquoBilingualism as a gradient measure modulatesfunctional connectivity of language and control networksrdquoNeuroImage vol 205 2020

Computational Intelligence and Neuroscience 7