Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning
Project Overview
The document outlines the development of an innovative explainable AI framework designed for the early detection of dyslexia through handwriting analysis, achieving an impressive classification accuracy of 99.65% using transfer learning and transformer-based architectures. A key feature of this model is its integration of Grad-CAM visualizations, which enhance the interpretability of the AI's decision-making process, thereby building trust among educators and clinicians who utilize the tool. The framework's adaptability to different languages and writing systems underscores its global applicability, making it a significant advancement in educational technology aimed at supporting early intervention for dyslexia. Overall, the findings highlight the potential of generative AI in education, particularly in fostering personalized learning experiences and improving outcomes for students with learning difficulties.
Key Applications
Explainable AI framework for dyslexia detection using handwriting analysis
Context: Educational and clinical settings targeting individuals with dyslexia
Implementation: Integrated transfer learning and transformer-based models; used Grad-CAM for interpretability
Outcomes: Achieved 99.65% accuracy in dyslexia detection; provided transparent decision-making insights
Challenges: Generalization to different handwriting styles; computational efficiency concerns with larger datasets
Implementation Barriers
Technical Barrier
Traditional AI models lack interpretability, making it difficult for educators and clinicians to trust the predictions. The model's effectiveness may vary across different handwriting styles or populations, potentially affecting accuracy.
Proposed Solutions: Implement explainable AI techniques like Grad-CAM to enhance transparency and understanding of AI decisions. Expand datasets to include diverse handwriting samples and conduct further research on model adaptation.
Computational Barrier
Scaling the model to larger and more diverse datasets may lead to computational efficiency issues.
Proposed Solutions: Optimize model architectures for efficiency and consider resource-constrained environments during deployment.
Project Team
Mahmoud Robaa
Researcher
Mazen Balat
Researcher
Rewaa Awaad
Researcher
Esraa Omar
Researcher
Salah A. Aly
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mahmoud Robaa, Mazen Balat, Rewaa Awaad, Esraa Omar, Salah A. Aly
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai