Data Sheet 1_Deep learning-based automated tongue analysis system for assisted Chinese medicine diagnosis.pdf

This study proposes an automated tongue analysis system that combines deep learning with traditional Chinese medicine to enhance the accuracy and objectivity of tongue diagnosis. The system includes a hardware device to provide a stable acquisition environment, an improved semi-supervised learning segmentation algorithm based on U2net, a high-performance colour correction module for standardising the segmented images, and a tongue image analysis algorithm that fuses different features according to the characteristics of each feature of the TCM tongue image. Experimental results demonstrate the system’s performance and robustness in feature extraction and classification. The proposed methods ensure consistency and reliability in tongue analysis, addressing key challenges in traditional practices and providing a foundation for future correlation studies with endoscopic findings.

本研究提出了一种融合深度学习与中医学（Traditional Chinese Medicine，TCM）的舌诊自动化分析系统，旨在提升舌诊的准确性与客观性。该系统包含用于提供稳定采集环境的硬件装置、基于U2net的改进型半监督学习分割算法、用于标准化分割后图像的高性能色彩校正模块，以及依据中医学舌象各特征属性实现多特征融合的舌象分析算法。实验结果验证了该系统在特征提取与分类任务中的性能与鲁棒性。所提方法保障了舌诊分析的一致性与可靠性，解决了传统舌诊实践中的核心难题，并为后续与内镜检查结果的关联研究奠定了基础。