Deep learning-based automated tongue analysis system for assisted Chinese medicine diagnosis.

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.