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

Explainable and Scalable Machine-Learning Algorithms for Detection of Autism Spectrum Disorder using fMRI Data