Autism Detection Based on Facial Images Using VGG16 and VGG19

Abstract

Abstract: A mental disability called autism spectrum disorder exhibits specific difficulties with verbal and nonverbal communication, interpersonal skills, and obsessive activities. Around 1% of the total populace is impacted by it, and its side effects frequently show up during the formative stages, or during the initial two years following birth. Autism can be diagnosed at any stage in once life and is said to be a "behavioural disease" because in the first two years of life symptoms usually appear. There hasn't been a strong diagnosis method, though, because there aren't any discernible variations between the facial images of healthy people and those of people with ASD. Machine learning and Deep learning approaches are being used in conjunction with traditional diagnostic procedures to increase the accuracy and turnaround time for diagnoses. In this study, we are looking to build a deep learning model i.e. A Convolution Neural Network that can classify and detect Autism based on facial images. The algorithm involves several key steps, including data collection, pre-processing, model training, and evaluation. This project explores the potential of using deep learning models, specifically VGG16 and VGG19 convolution neural networks (CNNs), for the detection of ASD based on facial images.