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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