Applying deep learning techniques to segmentize and classify tongue regions for traditional and complementary medicine (TCM) diagnosis - Yeap, Chun Hong (2025)
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.