Comparative Study of Deep Learning Models for Autism Diagnosis in Children Using Image Analysis

Autism Spectrum Disorder (ASD) is a complex neurological disorder related to an individual's psychological difficulties which eventually impact their behavior o r reactions to the outside world. Identifying autism at a younger age offers several advantages, including the opportunity for the individual to lead a better life, enabling preparation for their future and that of their close family members, and contributing to increased awareness and understanding of various medical conditions. In this paper, we suggest a deep learning-based method that makes use of image datasets to identify ASD in children. The databases include facial patterns and additional visual clues that can be deduced from images. Deep learning models like VGG16, VGG19, EfficientNetB4 a nd MobileNet are used. These architectures are pretrained on large-scale image datasets and refined toe xtract discriminative features on the ASD-specific dataset. W e h ave acquired facial image datasets from a publicly available platform called Kaggle. Our primary goal is to compare the deep learning models that better fit t he dataset a nd improve t he a ccuracy of autism detection than any other work before. This paper aims to facilitate model comparisons and streamline the autism detection process using advanced deep-learning techniques available today.