  Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial
intelligence (AI) techniques can aid physicians to apply automatic diagnosis
and rehabilitation procedures. AI techniques comprise traditional machine
learning (ML) approaches and deep learning (DL) techniques. Conventional ML
methods employ various feature extraction and classification techniques, but in
DL, the process of feature extraction and classification is accomplished
intelligently and integrally. DL methods for diagnosis of ASD have been focused
on neuroimaging-based approaches. Neuroimaging techniques are non-invasive
disease markers potentially useful for ASD diagnosis. Structural and functional
neuroimaging techniques provide physicians substantial information about the
structure (anatomy and structural connectivity) and function (activity and
functional connectivity) of the brain. Due to the intricate structure and
function of the brain, proposing optimum procedures for ASD diagnosis with
neuroimaging data without exploiting powerful AI techniques like DL may be
challenging. In this paper, studies conducted with the aid of DL networks to
distinguish ASD are investigated. Rehabilitation tools provided for supporting
ASD patients utilizing DL networks are also assessed. Finally, we will present
important challenges in the automated detection and rehabilitation of ASD and
propose some future works.
