International Journal of Science, Engineering and TechnologyAuthors: M. Menakapriya, M. Rasika, G. Prabha, R. Lavanya, S. Mounika, V. Nagarajan
Abstract: Early identification and treatment of Autism Spectrum Disorder (ASD) pose difficulties due to its complex neurological nature and heterogeneous symptoms. In this work, an innovative Self-Attention-Driven Vision Transformer (SA-ViT) model is introduced to cater to both ASD detection and cognitive skills improvement within one approach. Our work benefits from self-attention properties of vision transformers to detect subtle patterns in the behavior of ASD individuals as well as generate structured cognitive stimulation material. By extracting features from facial imagery, videos, and behavioral data, our SA-ViT can classify ASD samples with 97.6% accuracy on the ASD Facial Image Dataset, which beats regular CNN models (91.2%) and regular ViTs (94.8%). In terms of cognitive skills improvement, we were able to develop personalized structured tasks that resulted in 34.2% improved visual memory retention and 28.7% enhanced pattern recognition after eight weeks. The use of explainable AI approaches (Grad-CAM) enhances our system's applicability in a medical setting.
DOI: https://doi.org/10.5281/zenodo.20205889
