A Multimodal Diagnostic Framework for Autism Spectrum Disorder Using Deep Learning: An In-Depth Exploration

Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition, characterised by persistent and pervasive challenges in social communication, behavioural patterns, and sensory processing. The early and accurate diagnosis of ASD is imperative for timely intervention and support. This case study delineates a sophisticated deep learning-based multimodal diagnostic framework designed to augment the detection and analysis of ASD. By amalgamating various data modalities—encompassing neuro imaging, facial expressions, and speech patterns—this framework harnesses the capabilities of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance diagnostic precision. Furthermore, we delve into the intricacies of the challenges encountered, the preprocessing methodologies employed, and the comparative performance of the model vis-à-vis traditional diagnostic approaches.