Study of Tongue Image Classification for Yang Deficiency and Yin Deficiency Constitutions Based on Deep Learning
Objective This study aims to fill the gaps in traditional Chinese medicine(TCM)constitution identification meth-ods by automating the analysis of tongue image data,promoting the modernization and intelligence of constitution identification.Methods It collected participant constitution information and tongue image data using constitution questionnaires and a DS01-A tongue diagnosis instrument,ultimately including 260 images of Yang deficiency constitution tongue features and 114 images of Yin deficiency constitution tongue features.Before training the classification model for Yang deficiency and Yin deficiency tongue features,data augmentation and tongue image segmentation were performed.In this study,it used a U-net network for tongue im-age segmentation.The classification model was trained based on the ResNet-34 network architecture and optimized using both cross-entropy and Dice loss functions.Results In terms of model evaluation,this study employed metrics such as accuracy,loss functions,recall and F1 score.Experimental results demonstrated that the ResNet-34 model achieved an 88％accuracy rate on the validation dataset and performed well on the training data.Compared to other models(ResNet-18,ResNet-50,and Reg-Net),the ResNet-34 model exhibited the best performance.Conclusion These findings suggest that deep learning methods can effectively identify tongue features associated with Yang deficiency and Yin deficiency constitutions,opening up new possibilities for modernizing and automating TCM constitution classification.
traditional Chinese medicine constitutiontongue manifestationdeep learningmodernization of traditional Chi-nese medicine
NETL
NSTL
万方数据
