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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.