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

ABSTRACT:

Background

Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs.

Methods

In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance ima

SUBMITTER: Alkhurayyif Y 

PROVIDER: S-EPMC11232624 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature