Detection of Autism Spectrum Disorder using Deep Learning Models

Autism Spectrum Disorder (ASD) is a serious developmental disorder that involves problems with social interaction and communication, as well as repetitive behavior. Early diagnosis and treatment are key to improving the lives of people with autism. One recent breakthrough in the field involves the use of cutting-edge machine learning algorithms to automatically identify ASD using different kinds of data like behavioral observations. The proposed approach combines various types of deep learning models such as YOLOv8n, Detectron2, VGG16, ResNet50, Xception, Inceptionv3, and MobileNetV2. Using these models’ capabilities, the goal of the research study is to increase the distinction between ASD-related behavior signals and normal development patterns. The effectiveness of our method was demonstrated by test results which showed it was highly accurate.