ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

• Published in: Frontiers in neuroinformatics Vol. 13; p. 70
• Main Authors: Eslami, Taban, Mirjalili, Vahid, Fong, Alvis, Laird, Angela R., Saeed, Fahad
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 27.11.2019; Frontiers Media S.A
• Subjects: ABIDE; ASD; Autism; autoencoder; Brain mapping; Classification; fMRI; Functional magnetic resonance imaging; Learning algorithms; Medical imaging; Mental disorders; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; SLP; Florida; United States > US; fMRI; ASD; autoencoder; data augmentation; SLP; ABIDE; classification

Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to m...