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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis.

Meng-Yi Li1, Ding-Ju Zhu1,2, Wen Xu3

  • 1School of Computer Science, South China Normal University, Guangzhou, Guangdong 510631, China.

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|November 29, 2021
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Summary

This study introduces an improved U-Net network for intelligent tongue crack segmentation. The enhanced model achieves higher accuracy in identifying fissured tongue images, aiding computer-aided diagnosis systems.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Intelligent manufacturing supports smart medical services, with a focus on AI for medical diagnosis.
  • Tongue crack analysis is crucial for diagnosing diseases and Traditional Chinese Medicine (TCM) syndromes.
  • Existing computer tongue diagnosis systems lack in-depth research on fissured tongues.

Purpose of the Study:

  • To develop an improved deep learning model for accurate semantic segmentation of fissured tongue images.
  • To enhance the feature extraction capabilities of existing U-Net architectures for tongue image analysis.
  • To contribute to the development of more accurate computer-aided tongue diagnosis systems.

Main Methods:

  • Proposed an improved U-Net network incorporating a Global Convolution Network module.
  • Integrated the module into the encoder of the U-Net architecture to enhance semantic feature extraction.
  • Validated the method using a dedicated fissured tongue image dataset.

Main Results:

  • The improved U-Net network demonstrated superior segmentation performance compared to standard models.
  • Achieved higher segmentation accuracy on the fissured tongue image dataset.
  • The model effectively extracts abstract, high-level semantic features crucial for diagnosis.

Conclusions:

  • The proposed improved U-Net network is effective for semantic segmentation of fissured tongues.
  • This advancement can significantly enhance the accuracy of computer-aided tongue diagnosis systems.
  • Further research in this area can improve patient safety and clinical efficiency.