Autism Spectrum Disorder Detection Using Deeplearning Techniques

Abstract

This project aims to develop an intelligent system for detecting autism spectrum disorder (ASD) using deep learning techniques. Autism is a complex condition that affects communica tion and behavior, and early diagnosis is critical for effective, appropriate, and prompt interven tion. The system uses convolutional neural networks (CNNs), such as MobileNet and VGG19, to classify individuals as having or not having autism based on face images and eye-tracking data. Apublicly available Kaggle dataset containing images representing typical visual behavior of individuals with ASD was used. The data was preprocessed through resizing, normalization, and augmentation to improve model performance. The model was evaluated using precision, accuracy, recall, F1 score, and ROC-AUC. Theresults demonstrated high performance and outperformed traditional methods, demons trating the model’s effectiveness in detecting autism. This project highlights the role of artificial intelligence in advancing healthcare by enabling faster and more accurate diagnosis of complex conditions