Novel convolutional neural network structure for Diagnosing Autism Spectrum Disorder by Deep Learning

Entihaa Nsaif Jasim; Behrouz Tousi; Mohammad Farhadi-Kangarlu

Author(s)

Entihaa Nsaif Jasim Department of electrical and computer engineering, Urmia University, Urmia, Iran.

Behrouz Tousi Department of electrical and computer engineering, Urmia University, Urmia, Iran.

Mohammad Farhadi-Kangarlu Department of electrical and computer engineering, Urmia University, Urmia, Iran.

Abstract

Purpose: Autism spectrum disorder (ASD) affects approximately 1% of the population and is characterized by restricted and repetitive behaviors. Early diagnosis of ASD has been shown to improve outcomes and management of the disorder. Machine learning, which leverages patterns and common features, offers a promising approach for diagnosing ASD through brain imaging and distinguishing patients from control groups. This study aims to identify early indicators of ASD and reduce diagnostic complexity, utilizing the ABIDE dataset. Method: A supervised learning model was applied to 3D brain images using the TensorFlow library. The model was trained on 1,300 samples with a learning rate of 0.0001 and a dropout rate of 0.5. Additionally, 120 images, representing 1% of the total data, were used for testing. The model comprised three convolutional layers, each using the ReLU activation function, followed by maxpooling layers to isolate active brain regions at rest. After the final max-pooling layer, a fully connected layer was used for classification, and a dense layer with a dropout rate was employed to eliminate unnecessary nodes. A final fully connected layer, utilizing the SoftMax function, was used for prediction and decision-making. Findings and Conclusion: The model was trained 50 times using the entire test dataset with random shuffle algorithms. Results showed an increase in accuracy from 60% to 100%, and a reduction in the cost function from 36% to 20%, achieving an average prediction accuracy of 80%.

Publication Details

Page(s) 108-125

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

Request the full text from PASTIC

Keywords

deep learning, feature extraction, fMRI, ASD, autism, Functional Connectivity, ABIDE

References

HadeerAbd-El-RazakBarakat AB .2016 .Zeyad El-Sayad A Short Review on the Current Understanding of Autism Spectrum Disorders. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [22], : .

Neisworth JT,Wolfe P. S. The Autism Encyclopedia .2005 .. , : .

Oliver D Howes MR . .Autism spectrum disorder: Consensus guidelines on assessment, treatment and research from the British Association for Psychopharmacology 2017. , : .

RahimehRoohparvar MK .2015 .Comparing phonetic, phonologic, morphologic and syntactic features of speech in children with autism and typically developing children. , : .

.2018 .There Are Indeed More Left-Handers Within the Autism Spectrum Disorder Compared with in the General Population, but the Many Mixed-Handers Is the More Interesting Finding. , : .

Mandy W LM. .2016 .Annual Research Review: The role of the environment in the developmental psychopathology of autism spectrum condition. , : .

Rachel LoomesDClinPsy LHM,Meta-Analysis LHM .2005 .What Is the Male-toFemale Ratio in Autism Spectrum Disorder? A Systematic Review. , : .

Morey LC,Bender DS,Skodol AE . .Validating the proposed diagnostic and statistical manual of mental disorders, 5th edition, severity indicator for personality disorder. J NervMent Dis., : .

Lyall K,Croen L,Daniels J,Fallin MD,Ladd-Acosta C,Lee BK .2017 .. The Changing Epidemiology of Autism Spectrum Disorders. Annu Rev Public Health, 38 : 81-102.

Christensen DL BK,Baio J,Bilder D,Charles J,Constantino JN,Daniels J,Durkin MS,Fitzgerald RT,Kurzius-Spencer M,Lee LC,Pettygrove S,Robinson C,Schulz E,Wells C,Wingate MS,Zahorodny W,Yeargin-Allsopp M .2018 .. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years, : .

Qiu S,Lu Y,Li Y,Shi J,Cui H,Gu Y .2020 .Prevalence of autism spectrum disorder in Asia: A systematic review and meta-analysis. Psychiatry Res, 284 : 112679.

.2010 .A review of the prevalence of Autism Spectrum Disorder in Asia. , : .

GiacomoVivanti MP,Katrina Williams .2014 .Chery1 Dissanayake. Predictors of Outcomes in Autism Early Intervention: Why Don't We Know More?. , : .

Jacobs D,Steyaert J,Dierickx K,Hens K. .2019 .Physician View and Experience of the Diagnosis of Autism Spectrum Disorder in Young Children. Front Psychiatry, 10 : 372.

Baird G,Cass H,Slonims V .2003 .Diagnosis of autism. BMJ, 327(7413) : 93-488.

Varoquaux G TB .2019 .How machine learning is shaping cognitive neuroimaging. , : .

Keyvanrad M. .2015 .Review of the Deep Belief Network and Presentation of an Object-oriented. Matlab Toolkit, : .

.2016 .Provide a New Approach to Semi-oversight Learning. , : .

Lecum Y.,YB Y. .2015 .. DeepLearning' Nature, 521 : 444.

Parikh MN,Li H,He L. Enhancing H .2019 .Diagnosis of Autism with Optimized Machine Learning Models and Personal Characteristic Data. Front ComputNeurosci, 13 : 9.

Chen CP,Keown CL,Jahedi A,Nair A,Pflieger ME,Bailey BA .2015 .Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimaging Clin, 8 : 238-45.

.2014 .. , 9(12) : e113879.

Xin Yang MSI,A M ArefinKhaled. MSI .2019 .Functional connectivity magnetic resonance imaging classification of autism spectrum disorder using the multisite ABIDE dataset. , : .

NastaranMohammadian Rad AB,SeyedMostafa Kia .2016 .Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism. , : .

Wang Zhao LL .2020 .Research and development of autism diagnosis information system based on deep convolution neural network and facial expression data. , : .

.2018 .Identification of autism spectrum disorder using deep learning and the ABIDE dataset. , : .

Learn Med Imaging Mach .2018 .. , 11046 : 9-303.

El Gazzar Ahmed,LC Ahmed,Convolutional D .2019 .Networks for Resting-State fMRI Based Classification in Autism. , : .

K. Sairam JN .2019 .Vithya and S. Srivathsan Computer Aided System for Autism Spectrum Disorder Using Deep Learning Methods. , : .

Chen CP,Keown C. L.,Jahedi A.,Nair A.,Pflieger M. E.,Bailey M. E. .2015 .Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default, mode, and visual regions in autism. , : .

ZeinabSherkatghanad M-sA .2020 .. Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network, : .

Nicha C. Dvornek PV,Kevin A .2017 .Pelphery and James S. Duncan. Identifying autism from restingstate FMRI using long short-term memory networks. , : .

.2014 .. , : .

Roy S.,Maji P. . .A simple Skull Stripping Algorithm for Brain MRI 2015. , : .

Jönemo and Anders,Eklund and Anders .2023 ."Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks. " Diagnostics, 17 : 2773.

.2023 ."Automatic detection of autism spectrum disorder based on fMRI images using a novel convolutional neural network. " Research on Biomedical Engineering, 39(2) : 407-413.

Feng .2023 .JuncaiXu. "Detection of ASD children through deep-learning application. of fMRI." Children, 10 : 1654.

Qiu .2024 .JianZhai. "A hybrid CNN-SVM model for enhanced autism diagnosis." Plos one 19. , (5) : e0302236.

Nazir .2024 .TanjinaHelaly. "Enhancing Autism Spectrum Disorder Diagnosis through a Novel 1D CNN-Based Deep Learning Classifier.". In 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), : 1-6.