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AutismNet: Recognition of Autism Spectrum Disorder from Facial Expressions using MobileNet Architecture

Autism Spectrum Disorder (ASD) is associated with neurodevelopmental disorders that deter the development of social interaction and communication abilities. Diagnosis of autism is one of the challenging tasks for researchers and doctors since the diagnosisis based on abnormalities in brain functions that may not flourish until the disorder is fully established. Facial expression analysis can be an effective solution for thediagnosis of autism in an early stage by exploiting a child’s expressions using automated systems. This research work aims to recognize autism from facial expressions using a deep learning model. The proposed model was implemented using Convolutional Neural Network (CNN) that is developed by the MobileNet model. The technique transfer learning was also implemented in this research work to amplify the performance of the model. The experimental result showed that the MobileNet model with a transfer learning approach couldprovide satisfactory results in the recognition task by achieving thehighest validation accuracy of 89%and test accuracy of 87%. The F1-score and precision value also advocated the reliability of our recognitionof theapproach by achieving the highest score of 87% for both metrics