DETECTION OF AUTISM SPECTRUM DISORDER USING DEEP LEARNING

Abstract

Social interaction, conduct, and cognitive ability are all impacted by the neuro developmental illness known as autism spectrum disorder (ASD). Even though ASD diagnosis can be difficult and time-consuming, early detection and intervention can improve long-term results. Early childhood is when autism spectrum disorder first manifests, and it eventually leads to issues with social, academic, and occupational functioning in society. Within the first year, autism signs are frequently visible in children. Some infants display autistic spectrum disorder symptoms as early as infancy, including decreased eye contact, a lack of responsiveness to their name, or a lack of interest in carers. To identify the presence of disorder at an early stage, use a deep learning system like LSTM. Self-Stimulatory Behaviours Dataset (SSBD) was used to collect the datasets, and video dataset was used to construct the system. The feature extraction algorithm is the Blaze Pose algorithm. The model file has been constructed, and the datasets will be trained using the deep learning technique. When a disease prediction input image is provided, the severity of the disease is determined as a result. Using the children's activity, the proposed system offers an effective way to more effectively anticipate the presence of autism spectrum disorder Key Words: Autism Spectrum Disorder, Long-Short Term Memory, Blaze Pose, Batch Normalization, ReLu, Softmax