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The Tongue and Taste Buds00:49

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The surface of the tongue is covered with various small bumps called papillae, which either distribute what has been ingested (filiform papillae) or contain the sensory taste (or gustatory) receptor cells (fungiform, circumvallate, and foliate papillae). Embedded within each taste-related papilla are the taste buds—clusters of 30 to 100 gustatory receptor cells.
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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A Novel Tongue Coating Segmentation Method Based on Improved TransUNet.

Jiaze Wu1, Zijian Li1, Yiheng Cai2

  • 1School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 102488, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary

This study introduces an improved TransUNet model for precise tongue coating segmentation in intelligent tongue diagnosis. The model enhances accuracy and detail, aiding automated diagnostic systems.

Keywords:
TransUNetcomputer visiondeep learningmedical image segmentationtongue diagnosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Tongue coating analysis is crucial for diagnosing various health disorders.
  • Automating tongue diagnosis requires accurate segmentation of tongue coating.
  • Current methods face challenges in segmenting complex tongue coating features.

Purpose of the Study:

  • To develop an advanced neural network for complex tongue coating segmentation.
  • To improve the accuracy and automation of intelligent tongue diagnosis systems.

Main Methods:

  • An improved TransUNet model incorporating a transformer for self-attention was developed.
  • Subtraction Feature Pyramid (SFP) and Visual Regional Enhancer (VRE) were integrated.
  • These enhancements aim to capture semantic information and improve spatial details in low-level features.

Main Results:

  • The proposed model achieved high performance metrics: 96.36% accuracy, 96.26% precision, 96.76% Dice score, 97.43% recall, and 93.81% IoU.
  • Comparative and ablation studies confirmed superior segmentation compared to the reference model.

Conclusions:

  • The improved TransUNet model offers precise segmentation of complex tongue images.
  • This technique effectively automates tongue coating extraction, enhancing diagnostic accuracy.