Early-Stage Detection of Autism Spectrum Disorders

This research addresses the critical need for early-stage prediction of autism in children through the analysis of image datasets. The escalating prevalence of autism underscores the urgency of developing effective diagnostic tools. Previous research has established the significance of early intervention in improving outcomes for individuals with autism. Our objective is to employ advanced image processing techniques to investigate distinctive patterns in the facial features of children, aiming to identify potential indicators of autism spectrum disorder (ASD). By utilizing machine learning algorithms, we seek to test the hypothesis that specific facial expressions and characteristics can serve as reliable early indicators of ASD. The research methods encompass the collection of a diverse dataset of children's images, preprocessing of data to enhance feature extraction, and the implementation of state-of-the-art machine learning models for prediction. This project not only contributes to the advancement of autism research but also holds promise for the development of non-invasive, accessible tools for early autism detection in clinical and educational settings.