Early Dyslexia Detection and Classification Using Residual Dense-Assisted Multi-Attention Transformer and Eye Tracking Data

DOI

10.2991/978-94-6463-704-5_6How to use a DOI?

Keywords

Depression; Automation Systems; Gated Convolutional; Capsule Transformer; Speech-Based

Abstract

Dyslexia is a neurological disorder which leads to learning disabilities mainly in reading. This learning disorder affects 5 to 10% of peoples across worldwide. Usually, persons affected from dyslexia faces difficulties in spelling, reading and writing fluency. It affects any age peoples and it is not related to their intelligence. It is a hidden reading disability which is crucial to diagnosis. But an early and easy diagnosis of dyslexia will improve the abilities of affected peoples using specialized software or tools. The major objective of this model is to present a clean and accurate screening model for dyslexia classification using eye tracking movement data. This paper proposed a residual dense-assisted multi-attention transformer model for detecting and classifying dyslexia with data normalization. In this paper the risk of reading difficulties can be analyzed by the tracking movement data which is better than any screening methods. The proposed research attained high accuracy, precision, recall, F1-score, sensitivity and specificity of 99.48%, 99.32%, 99.27%, 99.41%, 99.53% and 99.26% respectively. A novel transformer based residual dense network with multi-attention module is developed to detect and classify the dyslexia disorder. This research is done with the eye tracking data with large number of attributes. This work attained high accuracy which implies that the proposed model outperformed well on others.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - G. R. Priyasri
AU  - M. Uma Devi
PY  - 2025
DA  - 2025/04/30
TI  - Early Dyslexia Detection and Classification Using Residual Dense-Assisted Multi-Attention Transformer and Eye Tracking Data
BT  - Proceedings of the International Conference on Smart Health and Intelligent Technologies (ICSHit-2024)
PB  - Atlantis Press
SP  - 46
EP  - 63
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-704-5_6
DO  - 10.2991/978-94-6463-704-5_6
ID  - Priyasri2025
ER  -