用可解释AI分析书写特征，99.65%准确识别阅读障碍
Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning
- 融合迁移学习与Transformer模型，自动提取书写特征
- 测试精度达99.65%，优于现有方法
- 通过Grad-CAM可视化增强可信度，适合教育诊断
本研究提出一种可解释人工智能（XAI）框架，通过手写分析检测阅读障碍，测试精确率达到99.65%。该框架结合迁移学习与基于Transformer的模型，识别与阅读障碍相关的手写特征，并利用Grad-CAM可视化确保决策过程透明。其对不同语言和书写系统的适应性表明具备全球应用潜力。相比现有先进方法，该方案在分类准确率上取得突破，验证了手写分析作为诊断工具的可靠性。研究结果强调该框架在支持早期发现、建立利益相关方信任及制定个性化教育策略方面的价值。
原文摘要 · Abstract (English)
This study introduces an explainable AI (XAI) framework for the detection of dyslexia through handwriting analysis, achieving an impressive test precision of 99.65%. The framework integrates transfer learning and transformer-based models, identifying handwriting features associated with dyslexia while ensuring transparency in decision-making via Grad-CAM visualizations. Its adaptability to different languages and writing systems underscores its potential for global applicability. By surpassing the classification accuracy of state-of-the-art methods, this approach demonstrates the reliability of handwriting analysis as a diagnostic tool. The findings emphasize the framework's ability to support early detection, build stakeholder trust, and enable personalized educational strategies.
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