Abstract
Dyslexia is one of the most common learning disorders, often characterized by distinct features in handwriting. Early detection is essential for effective intervention. In this paper, we propose an explainable AI (XAI) framework for dyslexia detection through handwriting analysis, utilizing transfer learning and transformer-based models. Our approach surpasses state-of-the-art methods, achieving a test accuracy of 0.9958, while ensuring model interpretability through Grad-CAM visualizations that highlight the critical handwriting features influencing model decisions. The main contributions of this work include the integration of XAI for enhanced interpretability, adaptation to diverse languages and writing systems, and demonstration of the method's global applicability. This framework not only improves diagnostic accuracy but also fosters trust and understanding among educators, clinicians, and parents, supporting earlier diagnoses and the development of personalized educational strategies.
Abstract (translated)
阅读障碍是常见的学习障碍之一，通常通过手写特征表现出独特的特点。早期检测对于有效干预至关重要。本文提出了一种通过手写分析来检测阅读障碍的可解释人工智能（XAI）框架，该框架利用了迁移学习和基于变压器的模型。我们的方法超越了现有最佳方法，在测试中达到了0.9958的准确率，并且通过Grad-CAM可视化确保了模型的可解释性，突出了影响模型决策的关键手写特征。这项工作的主要贡献包括：为提高可解释性而整合XAI、适应多种语言和书写系统以及展示了该方法的全球适用性。此框架不仅提高了诊断准确性，而且促进了教育工作者、临床医生及家长之间的信任与理解，支持更早地进行诊断并开发个性化的教育策略。
URL
https://arxiv.org/abs/2410.19821
