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

arXiv: https://arxiv.org/abs/2506.06886 · HTML: https://mlsanity.com/paper/2506.06886/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 2 of 2)

- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis](https://mlsanity.com/paper/2210.16943/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classification](https://mlsanity.com/paper/2509.23968/) (2025)
- [A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities](https://mlsanity.com/paper/2608.18188/) (2026)

## Abstract (arXiv)

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.