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This paper presents a comprehensive tongue diagnosis system that integrates Traditional Chinese Medicine (TCM) constitution theory with advanced deep learning techniques. Tongue diagnosis, a vital component of TCM's “inspection” method, has long relied on practitioners’ subjective experience, leading to inconsistent diagnostic standards and susceptibility to external factors. To address these challenges, we propose a system combining YOLOv11 for detection, U2-Net for segmentation, and ResNet50 for classification of tongue images. Our system leverages the strengths of these models to overcome limitations in traditional tongue diagnosis, offering an efficient and accurate solution. We constructed a standardized dataset of 3,000 tongue images across nine constitutional types, collected under varied lighting conditions to ensure robustness. The experimental results demonstrate the system's effectiveness in automatically detecting, segmenting, and classifying tongue images, with significant improvements in diagnostic accuracy and efficiency. This study paves the way for the objectification and standardization of TCM inspection, providing a promising tool for mobile-based TCM diagnostic assistance.

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Integrating TCM Constitution Theory with Deep Learning for Tongue Image Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Zixuan Chen,
  • Jin Wang,
  • Yuzhen Liu,
  • Xiaolan Zhou,
  • Xiaoliang Wang

摘要

This paper presents a comprehensive tongue diagnosis system that integrates Traditional Chinese Medicine (TCM) constitution theory with advanced deep learning techniques. Tongue diagnosis, a vital component of TCM's “inspection” method, has long relied on practitioners’ subjective experience, leading to inconsistent diagnostic standards and susceptibility to external factors. To address these challenges, we propose a system combining YOLOv11 for detection, U2-Net for segmentation, and ResNet50 for classification of tongue images. Our system leverages the strengths of these models to overcome limitations in traditional tongue diagnosis, offering an efficient and accurate solution. We constructed a standardized dataset of 3,000 tongue images across nine constitutional types, collected under varied lighting conditions to ensure robustness. The experimental results demonstrate the system's effectiveness in automatically detecting, segmenting, and classifying tongue images, with significant improvements in diagnostic accuracy and efficiency. This study paves the way for the objectification and standardization of TCM inspection, providing a promising tool for mobile-based TCM diagnostic assistance.