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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network.

Jianjun Yan1,2, Bochang Chen2, Rui Guo3

  • 1Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai 200237, China.

Computational and Mathematical Methods in Medicine
|December 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for tongue texture analysis in Traditional Chinese Medicine (TCM). By using image inpainting and convolutional neural networks, it improves the accuracy of classifying tongue toughness and tenderness.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Traditional Chinese Medicine

Background:

  • Tongue texture analysis is crucial for Traditional Chinese Medicine (TCM) diagnosis, particularly for classifying tongue toughness and tenderness.
  • Texture discontinuity in tongue images, often due to tongue coating, negatively impacts classification accuracy.
  • Existing methods struggle with the inherent complexities of tongue image texture.

Purpose of the Study:

  • To develop a robust and accurate tongue image texture classification method.
  • To address the challenge of texture discontinuity in tongue body images.
  • To enhance the classification of tough and tender tongues using advanced AI techniques.

Main Methods:

  • A novel method combining image inpainting and convolutional neural networks (CNNs) for tongue texture classification.
  • Utilizing Gaussian mixture models to segment tongue coating from the tongue body.
  • Employing a generative image inpainting model with contextual attention to restore tongue body continuity.
  • Implementing a ResNet101 residual network for the final classification of inpainting images.

Main Results:

  • The proposed method effectively separates tongue coating and body, ensuring continuity of the tongue body image.
  • Image inpainting significantly reduces the interference of tongue coating on texture analysis.
  • The ResNet101-based classifier achieved superior performance in tough and tender tongue classification compared to existing methods.
  • Demonstrated improved accuracy and robustness in tongue texture analysis.

Conclusions:

  • The integration of image inpainting with CNNs offers a promising approach for accurate tongue texture analysis in TCM.
  • This method provides a new strategy for overcoming texture discontinuity challenges in tongue image classification.
  • The findings highlight the potential of AI-driven image processing to advance TCM diagnostic capabilities.