Facial image analysis for autism spectrum disorder detection in toddlers using deep learning and transfer learning

Abstract

Autism spectrum disorder (ASD) is a neurological illness that manifests itself through restricted and repeated activity patterns, frivolous or recidivist interests or hobbies and consistent handicaps to social interactions and exchanges. Better results and early intervention are dependent upon the early identification of people with ASD. Doctors employ a variety of techniques to anticipate autism, including genetic testing, neuropsychological testing, hearing and vision screenings, and diagnostic interviews. In addition to requiring more time and money, the traditional diagnosis approach makes the parents of children with extensive developmental abnormalities feel too inadequate to disclose their condition. So, we need a tool that can detect autism early in less time and money. Machine learning methods can be used to fulfill this criterion. In this study, deep learning with transfer learning (VGG-16) is used to detect autism through facial images of children and achieved almost 97% accuracy. The suggested model significantly improves accuracy and saves time and money by using face features in photos of children to identify early autism tendencies in children.