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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| Summary: | 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 frameworkâs balance of efficiency, accuracy, and interpretability, offering a practical solution to standardize and modernize tongue diagnosis in TCM for both clinical and telemedicine applications. |
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