  Diagnosing Autism Spectrum Disorder (ASD) is a challenging problem, and is
based purely on behavioral descriptions of symptomology (DSM-5/ICD-10), and
requires informants to observe children with disorder across different settings
(e.g. home, school). Numerous limitations (e.g., informant discrepancies, lack
of adherence to assessment guidelines, informant biases) to current diagnostic
practices have the potential to result in over-, under-, or misdiagnosis of the
disorder. Advances in neuroimaging technologies are providing a critical step
towards a more objective assessment of the disorder. Prior research provides
strong evidence that structural and functional magnetic resonance imaging (MRI)
data collected from individuals with ASD exhibit distinguishing characteristics
that differ in local and global spatial, and temporal neural-patterns of the
brain. Our proposed deep-learning model ASD-DiagNet exhibits consistently high
accuracy for classification of ASD brain scans from neurotypical scans. We have
for the first time integrated traditional machine-learning and deep-learning
techniques that allows us to isolate ASD biomarkers from MRI data sets. Our
method, called Auto-ASD-Network, uses a combination of deep-learning and
Support Vector Machines (SVM) to classify ASD scans from neurotypical scans.
Such interpretable models would help explain the decisions made by
deep-learning techniques leading to knowledge discovery for neuroscientists,
and transparent analysis for clinicians.
