A Comparative Study of Machine Learning Approaches for Autism Detection in Children from Imaging Data

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects language, communication, cognitive, and social skills. Early detection of ASD in children is crucial for effective intervention, and machine learning techniques have emerged as promising tools to improve the accuracy and efficiency of detection. This paper presents a range of Machine Learning approaches that have been applied to identify individuals with ASD, with a particular focus on children, using images as input data. The results of these studies demonstrate the potential for Machine Learning to aid in the early detection and diagnosis of ASD in children, which can lead to better outcomes for individuals with this condition.