Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis
Tongue diagnosis is a fundamental component of Traditional and Complementary Medicine (TCM), yet manual inspection remains subjective and inconsistent. This study proposes a deep learning framework to enhance tongue image analysis through segmentation, classification, and explainable artificial inte...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7248/1/fyp_CS_2025_YCH.pdf http://eprints.utar.edu.my/7248/ |
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| _version_ | 1854094497353302016 |
|---|---|
| author | Yeap, Chun Hong |
| author_facet | Yeap, Chun Hong |
| author_sort | Yeap, Chun Hong |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Tongue diagnosis is a fundamental component of Traditional and Complementary Medicine (TCM), yet manual inspection remains subjective and inconsistent. This study proposes a deep learning framework to enhance tongue image analysis through segmentation, classification, and explainable artificial intelligence (XAI). A Mobile U-Net model was proposed and developed for efficient and accurate tongue region segmentation. Classification tasks were conducted for both binary (stained vs. non-stained) and multi-class pathological coatings, covering clinically relevant categories. Lightweight architectures, including the proposed Efficient-ResNet, achieved competitive accuracy with minimal computational cost, demonstrating strong potential for deployment in resource-constrained environments. Grad-CAM was integrated to provide visual explanations of model decisions, improving transparency and clinical trust. Experimental results show that ResNet50 and LECA-EfficientNetV2-S achieved the highest accuracy of 99% in binary classification, while EfficientNetV2-B3 and -S excelled in multi-class tasks. Efficient-ResNet maintained strong accuracy (98.5%) with only 0.31M parameters. The findings highlight the framework’s balance of efficiency, accuracy, and interpretability, offering a practical solution to standardize and modernize tongue diagnosis in TCM for both clinical and telemedicine applications. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7248 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.72482025-12-29T10:19:09Z Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis Yeap, Chun Hong T Technology (General) TD Environmental technology. Sanitary engineering Tongue diagnosis is a fundamental component of Traditional and Complementary Medicine (TCM), yet manual inspection remains subjective and inconsistent. This study proposes a deep learning framework to enhance tongue image analysis through segmentation, classification, and explainable artificial intelligence (XAI). A Mobile U-Net model was proposed and developed for efficient and accurate tongue region segmentation. Classification tasks were conducted for both binary (stained vs. non-stained) and multi-class pathological coatings, covering clinically relevant categories. Lightweight architectures, including the proposed Efficient-ResNet, achieved competitive accuracy with minimal computational cost, demonstrating strong potential for deployment in resource-constrained environments. Grad-CAM was integrated to provide visual explanations of model decisions, improving transparency and clinical trust. Experimental results show that ResNet50 and LECA-EfficientNetV2-S achieved the highest accuracy of 99% in binary classification, while EfficientNetV2-B3 and -S excelled in multi-class tasks. Efficient-ResNet maintained strong accuracy (98.5%) with only 0.31M parameters. The findings highlight the framework’s balance of efficiency, accuracy, and interpretability, offering a practical solution to standardize and modernize tongue diagnosis in TCM for both clinical and telemedicine applications. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7248/1/fyp_CS_2025_YCH.pdf Yeap, Chun Hong (2025) Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis. Final Year Project, UTAR. http://eprints.utar.edu.my/7248/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Yeap, Chun Hong Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title | Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title_full | Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title_fullStr | Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title_full_unstemmed | Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title_short | Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis |
| title_sort | applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (tcm) diagnosis |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/7248/1/fyp_CS_2025_YCH.pdf http://eprints.utar.edu.my/7248/ |
| url_provider | http://eprints.utar.edu.my |