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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
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Deep learning-driven dyslexia detection model using multi-modality data.

Yazeed Alkhurayyif¹, Abdul Rahaman Wahab Sait²

¹Department of Computer Science, Shaqra University, Shaqra, Saudi Arabia.

Peerj. Computer Science

|July 10, 2024

Summary

This study introduces a novel deep learning model for dyslexia detection using multi-modality data. The developed dyslexia detection model achieves high accuracy, aiding early identification and intervention for individuals with dyslexia.

Keywords:

Convolutional neural network Deep learning Dyslexia Feature extraction Functional MRI Multi-modality data Wearable EEG device

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Area of Science:

Neuroscience
Computer Science
Medical Imaging

Background:

Dyslexia is a neurological disorder impacting language processing, necessitating early intervention for academic and social success.
Deep learning (DL) offers potential for multi-modality data integration in dyslexia detection models (DDMs), yet few such models exist.
Current research highlights the need for advanced computational approaches to improve dyslexia diagnosis.

Purpose of the Study:

To develop and evaluate a deep learning-based dyslexia detection model (DDM) integrating multi-modality neuroimaging and electrophysiological data.
To assess the performance of novel DL architectures, including SE-MobileNet V3, SA-EfficientNet B7, and SA-Bi-LSTM, for feature extraction.
To fine-tune a LightGBM classifier with Hyperband optimization for accurate dyslexia classification.

Main Methods:

Feature extraction from functional MRI (fMRI), MRI, and electroencephalography (EEG) data using specialized deep learning models (SE-MobileNet V3, SA-EfficientNet B7, SA-Bi-LSTM).
Integration of extracted features for dyslexia detection via a LightGBM model optimized with the Hyperband technique.
Validation of the proposed DDM across three distinct datasets (fMRI, MRI, EEG).

Main Results:

The proposed multi-modality DDM achieved high detection accuracies: 98.9% (fMRI), 98.6% (MRI), and 98.8% (EEG).
The model demonstrated superior performance compared to existing DDMs, even with limited computational resources.
The findings underscore the model's potential for effective and efficient dyslexia identification.

Conclusions:

The developed deep learning model offers a significant advancement in dyslexia detection using multi-modality data.
The high accuracy and efficiency of the model support its application in healthcare and educational settings for early dyslexia identification.
Future work could enhance model interpretability through the integration of vision transformers for feature extraction.