Deep learning approaches for autism spectrum dis-order diagnosis: Ensemble archtectures and multi-modal analysis

This thesis presents novel multi-modal deep learning approaches for enhancing the precision and efficiency of Autism Spectrum Disorder (ASD) diagnosis in children. The first approach, ASD-CVH, combines a hybrid vision transformer and convolu-tional neural network (CNN) architecture to extract high-level features and attention maps from audio samples, achieving excellent accuracy in differentiating between ASD and typically developing (TD) children. The second approach, ASD-EVNet, utilizes ensemble learning with state-of-the-art Vision Transformer (ViT) models fine-tuned on a face-based ASD dataset for children (FADC), achieving state-of-the-art results in ASD diagnosis based on facial expressions. By integrating audio-based and visual-based deep learning models, this research establishes a comprehensive framework for ASD diagnosis, providing a multi-modal perspective that enhances accuracy and relia-bility. The findings contribute to the development of objective and efficient diagnostic tools for ASD, supporting early intervention and improved care for children with ASD.