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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks.

Jiawei Li1, Zhidong Zhang1, Xiaolong Zhu1

  • 1Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China.

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Summary

This study introduces an AI framework for traditional Chinese medicine (TCM) tongue diagnosis, improving accuracy and consistency. The convolutional neural network model achieves over 86% accuracy in classifying tongue features.

Keywords:
TCM tongue diagnosisconvolutional neural networkdeep learningimage classificationtongue segmentation

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Area of Science:

  • Integrative Medicine
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • Tongue diagnosis is a key component of traditional Chinese medicine (TCM) but relies on subjective practitioner expertise.
  • Current tongue diagnosis methods exhibit subjectivity and instability, leading to diagnostic variations.
  • There is a need for objective and reliable tools to support TCM diagnostic practices.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) based framework for objective tongue feature classification in TCM.
  • To reduce inter-practitioner diagnostic variability by introducing an automated approach.
  • To enhance the accuracy and consistency of TCM tongue diagnoses.

Main Methods:

  • A custom instrument was used to capture 482 tongue images, forming 11 distinct feature datasets.
  • Tongue segmentation was performed using an enhanced facial landmark detection technique combined with the UNET architecture.
  • The ResNet34 model served as the backbone for feature extraction and subsequent tongue image classification.

Main Results:

  • The proposed CNN framework achieved an overall accuracy exceeding 86% in tongue feature classification.
  • The model demonstrated high sensitivity in identifying and classifying specific tongue feature regions.
  • Experimental results indicate a significant improvement in diagnostic consistency and accuracy.

Conclusions:

  • The developed AI framework offers a promising solution for objective and accurate tongue diagnosis in TCM.
  • This technology has the potential to assist TCM practitioners, leading to more reliable and consistent diagnostic outcomes.
  • The framework's sensitivity to feature regions highlights its clinical utility in supporting TCM practice.