Automated detection of autism spectrum disorder using a convolutional neural network

Sherkatghanad, Z ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.3389/fnins.2019.01325
  3. Publisher: Frontiers Media S.A , 2020
  4. Abstract:
  5. 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. © Copyright © 2020 Sherkatghanad, Akhondzadeh, Salari, Zomorodi-Moghadam, Abdar, Acharya, Khosrowabadi and Salari
  6. Keywords:
  7. ABIDE ; Atlas ; Autism spectrum disorder ; Convolutional neural networks ; FMRI ; Autism ; Cerebellum vermis ; Controlled study ; Convolutional neural network ; Female ; Functional connectivity ; Functional magnetic resonance imaging ; Fusiform gyrus ; k nearest neighbor ; Major clinical study ; Random forest ; Receiver operating characteristic ; Sensitivity and specificity ; Support vector machine ; Supramarginal gyrus
  8. Source: Frontiers in Neuroscience ; Volume 13 , 2020
  9. URL: https://www.frontiersin.org/articles/10.3389/fnins.2019.01325/full