Frontiers in Neuroscience (Jan 2020)

Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

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

DOI
https://doi.org/10.3389/fnins.2019.01325
Journal volume & issue
Vol. 13

Abstract

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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.

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