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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Lightweight hybrid transformers-based dyslexia detection using cross-modality data.

Abdul Rahaman Wahab Sait¹, Yazeed Alkhurayyif²

¹Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, Al-Ahsa, 31982, Saudi Arabia. asait@kfu.edu.sa.

Scientific Reports

|May 16, 2025

Summary

This study introduces a novel deep learning model for early dyslexia detection using MRI, EEG, and handwriting data. The model achieves 99.8% accuracy, offering a significant advancement for timely intervention and improved outcomes.

Keywords:

Cross-modality EEG Feature fusion Handwriting images MRI Multi-attention mechanisms Vision Transformers

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Area of Science:

Neuroscience
Machine Learning
Medical Imaging

Background:

Early dyslexia diagnosis is critical for effective intervention and improved academic/cognitive outcomes.
Traditional dyslexia detection methods are subjective, time-consuming, and limited in scope.
Deep learning models show promise for dyslexia detection but face challenges with cross-modality data and interpretability.

Purpose of the Study:

To develop an innovative deep learning model for precise dyslexia detection using multi-modal data.
To address limitations in current deep learning approaches, including feature extraction from cross-modality data, interpretability, and computational efficiency.
To enhance early identification and facilitate timely intervention for individuals with dyslexia.

Main Methods:

Proposed a hybrid transformer-based model integrating SWIN-Linformer (MRI), LeViT-Performer (handwriting), and Graph Transformer Networks (EEG).
Employed multi-modal attention-based feature fusion for integrating diverse data types.
Utilized Bayesian optimization with Hyperband (BOHB) for enhancing Dartbooster XGBoost classification and quantization-aware training for reduced computational overhead.
Incorporated LIME and Grad-CAM for model interpretability.

Main Results:

Achieved a high accuracy of 99.8% in dyslexia detection across five public repositories.
Demonstrated superior performance compared to baseline models.
Showcased reduced computational overhead through quantization-aware training.
Validated model interpretability using LIME and Grad-CAM techniques.

Conclusions:

The proposed multi-modal deep learning model offers a novel and highly accurate approach to dyslexia detection.
The model's interpretability and computational efficiency make it suitable for practical, resource-constrained applications.
This advancement holds significant potential for improving early identification and intervention strategies for dyslexia.