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arXiv · 2506.06886

Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis

Wafaa Kasri·Yassine Himeur·Abigail Copiaco·Wathiq Mansoor·Ammar Albanna·Valsamma Eapen
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Abstract

Accurate Autism Spectrum Disorder (ASD) diagnosis is vital for early intervention. This study presents a hybrid deep learning framework combining Vision Transformers (ViT) and Vision Mamba to detect ASD using eye-tracking data. The model uses attention-based fusion to integrate visual, speech, and facial cues, capturing both spatial and temporal dynamics. Unlike traditional handcrafted methods, it applies state-of-the-art deep learning and explainable AI techniques to enhance diagnostic accuracy and transparency. Tested on the Saliency4ASD dataset, the proposed ViT-Mamba model outperformed existing methods, achieving 0.96 accuracy, 0.95 F1-score, 0.97 sensitivity, and 0.94 specificity. These findings show the model's promise for scalable, interpretable ASD screening, especially in resource-constrained or remote clinical settings where access to expert diagnosis is limited.

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Wafaa Kasri, Yassine Himeur, Abigail Copiaco, Wathiq Mansoor, Ammar Albanna, Valsamma Eapen. 2025-06-07. Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis. https://arxiv.org/abs/2506.06886

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