Abstract
Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data.Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD and control subjects based on the patterns of functional connectivity.Results: Our experimental outcomes indicate that the proposed model is able to detect ASD correctly with an accuracy of 70.22% using the ABIDE I dataset and the CC400 functional parcellation atlas of the brain. Also, the CNN model developed used fewer parameters than the state-of-art techniques and is hence computationally less intensive. Our developed model is ready to be tested with more data and can be used to prescreen ASD patients.
Highlights
Autism spectrum disorder (ASD), a type of neurological disorder, appears in children between 6 and 17 years of age and affects communication skills and social behavior
The results show that the sites named the Kennedy Krieger Institute, Baltimore (KKI), San Diego State University (SDSU), and University of Utah School of Medicine (USM) achieved higher accuracies than other sites
The phenotypic information is classified based on sex, age, and autism diagnostic observation schedule (ADOS) score for ASD subjects and mean framewise displacement (FD) quality, which is a measure of subject head motion2
Summary
Autism spectrum disorder (ASD), a type of neurological disorder, appears in children between 6 and 17 years of age and affects communication skills and social behavior. Diagnosis during childhood is important and can improve the social skills and communication problems of children with ASD and enhance their quality of life. Functional magnetic resonance imaging (fMRI) is used to study the brain and its structures. The second phase (ABIDE II) has 521 ASD patients and 593 healthy controls and was obtained from 19 sites. Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data
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