Classification of Autism Spectrum Disorder using Convolutional Neural Networks from Neuroimaging Data

Current Autism Spectrum Disorder (ASD) diagnosis methods exhibit some limitations as they are based on clinical interviews and observations of behaviors, characteristics, and abilities. Moreover, considering the current challenges in identifying the causes and mechanisms associated with ASD, there is an essential need for automated techniques capable of providing an accurate classification between ASD and typically developed (TD). In this paper, we present a convolutional neural network model that can differentiate ASD from TD. This proposed system is trained and validated on the well-known Autism Brain-Imaging Data Exchange (ABIDE) dataset for the resting-state Functional Magnetic Resonance Imaging (fMRI). Results showed the robustness ofthe proposed system that achieves maximum accuracy of 99%. © 2022 IEEE.