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Related Experiment Video

Updated: Aug 26, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Automatic tongue image quality assessment using a multi-task deep learning model.

Huimin Xian1, Yanyan Xie1, Zizhu Yang1

  • 1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.

Frontiers in Physiology
|October 7, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model enhances tongue image quality assessment (IQA) for traditional Chinese medicine diagnosis. This method improves image evaluation and provides tongue segmentation, aiding clinical practice.

Keywords:
deep learningmulti-task learning modeltongue image quality assessmenttongue segmentationtraditional Chinese medicine

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Traditional Chinese Medicine

Background:

  • Tongue image quality is crucial for accurate diagnosis in Traditional Chinese Medicine (TCM).
  • Image acquisition is susceptible to variations in illumination, camera settings, and tongue posture.
  • Ensuring high-quality images is essential for reliable TCM diagnostic criteria.

Purpose of the Study:

  • To develop a deep learning model for evaluating tongue image quality.
  • To improve the accuracy and reliability of tongue image analysis in TCM.
  • To provide a tool that assists TCM practitioners in selecting diagnostic-quality images.

Main Methods:

  • Acquired tongue images under diverse conditions (lighting, exposure, extension).
  • Manually screened images into high-quality and unqualified datasets by experienced TCM physicians.
  • Designed a multi-task deep learning network incorporating tongue segmentation as an auxiliary task.
  • Adaptively adjusted task weight coefficients for optimal performance.

Main Results:

  • The proposed multi-task deep learning model significantly outperformed traditional deep learning methods for tongue image quality assessment (IQA).
  • The model successfully performed tongue segmentation as an auxiliary task, outputting valuable segmentation data.
  • Network visualization confirmed the qualitative effectiveness of the proposed IQA method.

Conclusions:

  • The developed deep learning approach provides a superior method for tongue image quality evaluation.
  • The integrated tongue segmentation capability offers practical benefits for subsequent clinical diagnosis.
  • This model enhances the consistency and diagnostic value of tongue imaging in TCM.