A multi-step approach for tongue image classification in patients with diabetes
A multi-step approach for tongue image classification in patients with diabetes
复制标题
糖尿病患者舌头图像分类的多步骤方法。
DOI：
10.1016/j.compbiomed.2022.105935
复制
发表时间：
2022-08-17
影响因子：
7.7
通讯作者：
Xu, Jiatuo
中科院分区：
文献类型：
--
作者：
Li, Jun;Huang, Jingbin;Xu, Jiatuo
背景：在我国，糖尿病是一种常见、高发的慢性病。糖尿病已成为严重的公共卫生问题。然而，目前的诊断和治疗方法难以控制糖尿病的进展。中医药因其成本低、疗效好、可及性好等特点，已成为糖尿病治疗的一种选择。目的：基于舌象数据实现糖尿病人群的精细分类，为糖尿病个体化治疗方案的制定提供诊断依据，保证中医诊断的准确性和一致性，促进中医诊断客观化、规范化发展。方法：采用TFDA-1型舌象仪采集舌象。利用舌象诊断分析系统（TDAS）提取舌象的TDAS特征。矢量量化变分自编码器（VQ-VAE）从舌图像中提取VQ-VAE特征。基于VQ-VAE特征，对舌象进行K均值聚类。TDAS特征用于描述聚类之间的差异。结果：基于VQ-VAE特征，K-means算法将糖尿病人群划分为4类，聚类边界清晰。轮廓、卡林斯基哈拉巴斯和戴维斯布尔丁的得分分别为0.391、673.256和0.809。聚类1具有最高的舌体L（TB-L）和舌苔L（TC-L）以及最低的舌苔角二阶矩（TC-ASM），具有淡红色舌和白色舌苔。第2组具有最高的TC-b，具有黄色舌苔。集群3具有最高的TB-a，具有红色舌头。第4组的TB-L、TC-L和TB-b最低，Per-all最高，舌质紫，舌苔面积最大。ViT验证了K-means的聚类结果，最高的Top-1分类准确率（CA）为87.8%，平均CA为84.4%。结论：本研究将无监督学习、自监督学习和监督学习有机结合，设计了一种完整的糖尿病舌象分类方法。该方法不依赖于人工干预，完全基于舌图像数据做出决策，并取得了最先进的结果。我们的研究将有助于中医药深度参与糖尿病的个体化治疗，为促进中医诊断标准化提供新思路。
Background: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe 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, good curative effect, and good accessibility.Objective: Based on the tongue images 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 TCM diagnosis, and promote the objective 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 TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K -means clustering tongue images. TDAS features are used to describe the differences between 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: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%.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, 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 standardization of TCM diagnosis.