Deep Learning for the Classification of Autism Using Functional Neuroimaging

Abstract

Deep learning models have advanced many branches of science. However, these models have not been adequately developed for neuroimaging applications mainly because of the non-availability of large labelled datasets. In this study, we present an explainable deep learning approach to investigate the neurobiology of the autism spectrum disorder (ASD), which is one of the most prevalent neurodevelopmental disorders. Our approach achieved state of the art classification accuracy and identified brain features in discriminating ASDs from the typical subjects and finally identified features that predicted the severity of the symptoms.