Detecting Autism Spectrum Syndrome using VGG19 and Xception Networks

Abstract

Autism spectrum Syndrome (ASS) is an evolving disability caused by transformations in human brain. Patients having this disease have complications such as lacking of social communiqué and interaction, restricted interests to participate in activities. But for patients suffering from ASD, these physiognomies make life very complicated. Children with this, have divergent facial landmarks, from normal children. These landmarks can be identified using deep learning models which can be used to detect ASD. But they models should be able to extract and produce the proper patterns of the face features. Thus, for this task, two variants of CNN models namely Xception andVGG19 were used for this task. The dataset required for training and testing of the application was collected from the Kaggle platform and consists of 2,940 face images comprising equal number of healthy and autistic patient images. The results showed that Xception model has an accuracy of 86% while the VGG19 model gives an accuracy of 81%. The results indicated that the proposed system can be used for ASD.