Smart Healthcare system for diagnosing Autism Spectrum Disorder Using Deep Neural Networks

Abstract

The number of people who have a disease that falls on the autistic spectrum is quickly increasing. Unfortunately, it may be difficult for them to obtain an early diagnosis and receive intervention in order to avoid problematic behaviors, which may lead to social isolation and financial loss for the entire family. Children who fall somewhere on the autism spectrum often have difficulties with social reciprocity, shared attention, and effective language expression. Consequently, autistic children commonly struggle with feelings of isolation. The novelty of the study is the early identification of autism in children based on their behaviors, such as arm flapping, head banging, and spinning, enabling intervention. This study integrates three transfer models—MobileNet, ResNet50, and InceptionResNetV2—with a long short-term memory (LSTM) model to aid early diagnosis of autism spectrum disorder. The Self-Stimulatory Behaviors Dataset (SSBD), which is available to the general public, was used for training. This dataset contains 125 films of persons with autism as well as autistic and non-autistic controls. We used the usual measures of accuracy, specificity, and sensitivity to see how well the three deep learning models worked. The empirical data from the diagnostic system indicated that integrating an LSTM model with ResNet50 achieved a superior accuracy of 93.10%. Every family must have access to an objective, affordable, and easy-to-use diagnostic or screening solution to enable early intervention for their children with ASD. To reach this goal, we developed an early screening tool for autism spectrum disorder that uses deep learning to accurately assess stimming behaviors and children's movements. This enables early diagnosis and treatment of the disorder.