Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

• Published in: Frontiers in neuroscience Vol. 13; p. 1325
• Main Authors: Sherkatghanad, Zeinab, Akhondzadeh, Mohammadsadegh, Salari, Soorena, Zomorodi-Moghadam, Mariam, Abdar, Moloud, Acharya, U. Rajendra, Khosrowabadi, Reza, Salari, Vahid
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 14.01.2020; Frontiers Media S.A
• Subjects: ABIDE; Autism; autism spectrum disorder; Brain mapping; Classification; convolutional neural networks; Datasets; fMRI; Functional magnetic resonance imaging; Investigations; Learning algorithms; Machine learning; Nervous system; Neural networks; Neuroimaging; Neuroscience; Speech recognition; fMRI; ABIDE; convolutional neural networks; autism spectrum disorder; atlas
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.01325

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. 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. 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.