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Updated: Aug 23, 2025
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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
Published on: April 14, 2023
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An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform
Zibin Yang1, Yuping Zhao2, Jiarui Yu1
1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Diagnostics (Basel, Switzerland)
|October 27, 2022
Summary
We developed an intelligent tongue diagnosis system using deep learning on mobile devices for accurate pathological feature identification. This system achieves high accuracy in classifying tooth marks, spots, and fissures, offering a valuable tool for objective tongue diagnosis.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Digital Health
Background:
- Traditional tongue diagnosis lacks objectivity and standardization.
- The integration of artificial intelligence (AI) offers potential for enhancing diagnostic accuracy and accessibility.
Purpose of the Study:
- To develop an intelligent tongue diagnosis system utilizing deep learning on mobile terminals.
- To create an efficient and accurate tongue image processing algorithm framework for pathological feature inference.
Main Methods:
- Developed an Android-based software system integrating user management, tongue image recognition, and doctor-patient communication.
- Trained deep learning models, including YOLOv5s6, U-Net, and MobileNetV3, for tongue recognition, segmentation, and feature classification (tooth marks, spots, fissures).
Main Results:
- The tongue diagnosis system demonstrated satisfying performance.
- Achieved high classification accuracies: 93.33% for tooth marks, 89.60% for spots, and 97.67% for fissures.
Conclusions:
- The developed intelligent tongue diagnosis system shows significant potential for objective and intelligent medical diagnosis.
- This mobile-based deep learning approach provides a reference for advancing traditional diagnostic methods.
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