AUTHOR=Sherkatghanad Zeinab , Akhondzadeh Mohammadsadegh , Salari Soorena , Zomorodi-Moghadam Mariam , Abdar Moloud , Acharya U. Rajendra , Khosrowabadi Reza , Salari Vahid 

TITLE=Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

JOURNAL=Frontiers in Neuroscience

VOLUME=13

YEAR=2020

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01325

DOI=10.3389/fnins.2019.01325

ISSN=1662-453X

ABSTRACT=<p><bold>Background:</bold> 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.</p><p><bold>Method:</bold> 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.</p><p><bold>Results:</bold> 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.</p>