Abstract

Autism Spectrum Disorder (ASD) is a complex condition with varied symptoms, making it hard to diagnose accurately using traditional methods like clinical observations and standardized tests. To address this, researchers are turning to advanced deep learning techniques. One such innovative method is using Deep Graph Convolutional Networks (DeepGCNs) to diagnose ASD. This approach combines different types of data, such as brain scans, genetic information, and behavioral records, into a unified analysis. DeepGCNs transform these diverse data into graphs, where nodes represent different features and edges show their relationships, allowing the model to understand complex patterns more effectively. This multimodal method leads to more accurate and quicker diagnoses by providing a detailed and comprehensive view of the condition. Overall, this technology holds great promise for improving the diagnosis and treatment of individuals with ASD.