A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI

Haweel, Reem; Shalaby, Ahmed; Ali Mahmoud; Seada, Noha; Ghoniemy, Said; Ghazal, Mohammed; Casanova, Manuel F; Barnes, Gregory N; El-Baz, Ayman;

Abstract


Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment.


Other data

Title A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI
Authors Haweel, Reem ; Shalaby, Ahmed ; Ali Mahmoud; Seada, Noha; Ghoniemy, Said ; Ghazal, Mohammed; Casanova, Manuel F; Barnes, Gregory N; El-Baz, Ayman
Keywords ASD; BOLD signal; CNN; DWT; K-means; task-based fMRI
Issue Date May-2021
Publisher WILEY
Journal Medical physics 
ISSN 0094-2405
DOI 10.1002/mp.14692
PubMed ID 33378589
Scopus ID 2-s2.0-85102892758
Web of science ID WOS:000631480900001

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