ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

• Published in: Frontiers in computational neuroscience Vol. 15; p. 654315
• Main Authors: Almuqhim, Fahad, Saeed, Fahad
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 08.04.2021; Frontiers Media S.A
• Subjects: ABIDE; ASD; Autism; autoencoder; Biomarkers; Brain mapping; Classification; Data acquisition; Datasets; Deep learning; fMRI; Functional magnetic resonance imaging; Medical imaging; Neural networks; Neurodevelopmental disorders; Neuroimaging; Neuroscience; Social interactions; sparse autoencoder; fMRI; ASD; autoencoder; deep-learning; sparse autoencoder; diagnosis; ABIDE; classification

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understandin...