# Tongue-Classification

**Repository Path**: senlinzhiwang/Tongue-Classification

## Basic Information

- **Project Name**: Tongue-Classification
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-14
- **Last Updated**: 2025-04-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Digital tongue image analyses for health assessment

### Abstract
Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient’s health status by examining the tongue’s color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective health assessments. This paper reviewed the current trends in TCM tongue diagnosis, including tongue image acquisition hardware, tongue segmentation, feature extraction, color correction, tongue classification, and tongue diagnosis system. We also present a case of TCM constitution classification based on tongue images.

![Figure 1  Organ correspondence of tongue regions](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/ad15d4c6-0c10-43c1-93b3-6550e4d245be)
**Figure 1:Organ correspondence of tongue regions.**
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![Figure 2  General process of computerized tongue diagnosis](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/c1510618-5c30-4fe3-ac2d-77fc2940d7f7)
**Figure 2:General process of computerized tongue diagnosis. The blue parts represent the tongue diagnosis process using traditional machine learning methods, and the green parts represent the deep learning process.**
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![Figure 3  Tongue image collection hardware](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/6619785b-164b-4c23-9481-544e5dc127f8)
**Figure 3:Tongue image collection hardware. (A) A tongue capture device consisting of a three-chip CCD camera. (B) An automatic tongue diagnosis system using a camera with circular LED lighting, a color card, camera support, and a sliding trail for vertical adjustment. (C) A device equipped with Canon EOS 1200D and a simulated D65 illuminant environment. (D) A tongue image capturing device with Logitech Pro C920 camera and the D65 illuminant. (E) The handheld TDA-1 tongue imaging capturing device [40]. (F) An integrated system with standardized light sources, a digital camera, and color correction.**
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![Figure 4  Three common colorcheckers ](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/8db36c0d-6c6a-424e-9d90-4c95db47972a)
**Figure 4:Three common color checkers. (A) A checker with 24 colors in an improved version. (B) A checker with 120 colors. (C) Color checker SG highlights the interesting regions.**
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![Figure 5  Tongue image of nine constitutions in traditional Chinese medicine](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/19795f6a-af54-4a00-a483-b8e1705121b6)
**Figure 5:Tongue image of nine constitutions in traditional Chinese medicine.**
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![Figure 6  Example of tongue image data augmentation](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/fcc34545-e054-4670-84d4-d38d6ca07d0e)
**Figure 6:Example of tongue image data augmentation.**
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![Tongue](https://github.com/orangeshushu/Tongue-Classification/assets/8640422/b62d5a9b-13ed-4b82-9ddc-94f85665f5ba)
**Figure 7:Operation flowchart of iTongue app.**
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Please cite our paper:**Digital tongue image analyses for health assessment**
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Xie J, Jing C, Zhang Z, Xu J, Duan Y, Xu D. Digital tongue image analyses for health assessment. Med Rev (2021). 2022 Feb 14;1(2):172-198. doi: 10.1515/mr-2021-0018. PMID: 37724302; PMCID: PMC10388765.