Diabetes Tongue Image Classification Using Machine Learning and Deep Learning

Abstract

BackgroundIn China, diabetes is a common, high-incidence chronic disease. Diabetes has become a serious public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, satisfactory curative effect and good accessibility. Objective: Based on the tongue image data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the diagnosis of TCM, and promote the objective, standardized and standardized development of TCM diagnosis. Methods: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the color feature, texture feature and tongue coating ratio feature of the tongue image. Auto-Encoder extracts auto-encoding features from tongue images through self-supervised learning extraction. We use K-means to perform fusion calculations on TDAS features and self-encoding features to classify tongue images. TDAS features are used to describe the differences between clusters and the characteristics of clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. Results: According to the input tongue image data, K-means divides the diabetic population into 4 clusters with clear boundaries between clusters. Cluster 3 had the highest TB-L and TC-L, and Cluster 2 had the lowest TB-L and TC-L (P <0.001). Cluster 2 and Cluster 4 had the highest TB-a (P <0.01, P <0.001), and Cluster 3 had the lowest TB-a (P <0.001). Cluster 2 had the highest TB-b and TC-b (P <0.01, P <0.001), and Cluster 3 had the lowest TB-b and TC-b (P <0.01, P <0.001). Cluster 2 had the highest TB-ASM, and TB-CON, TB-ENT and TB-MEAN had the lowest (P <0.001). Cluster 3 had the lowest TC-ASM, TC-CON, TC-ENT and TC-MEAN the highest (P <0.01, P <0.001). Cluster 3 had the highest Per-all (P <0.001). Vision Transformer (ViT) verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 84.9%, and the average CA is 83.6%. Conclusions: The study organically combined unsupervised learning, self-supervised learning and supervised learning, and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, and makes decisions based entirely on tongue image data and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes, and provide new ideas for promoting the objective, standardization and standardization of TCM diagnosis. Funding: The authors gratefully acknowledge the financial supports by the National Key Research and Development Program of China [grant numbers 2017YFC1703300 and 2017YFC1703301]; National Natural Science Foundation of China [grant numbers 81873235, 81973750, 82004258 and 81904094]; and 1226 Engineering Health Key Project [grant number BWS17J028]. Declaration of Interest: We declare no competing interests. Ethical Approval: The study was approved by the IRB of Shuguang Hospital affiliated with Shanghai University of TCM.