Deep learning-based recognition of stained tongue coating images

使用深度学习识别染苔图像

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

目的本研究旨在建立染苔图像数据集,并利用深度学习自动识别染苔图像。方法本研究收集了1001张湖南中医药大学健康学生的染苔图像和1007张湖南中医药大学第一附属医院的肺癌、糖尿病和高血压患者的病理性舌苔(非染苔)图像,图像数据按7∶2∶1的比例随机分为训练集、验证集和测试集。使用训练集和验证集数据对ResNet50进行训练,训练周期为90次,构建基于深度学习的染苔图像识别模型。根据测试集的预测结果,从准确率、损失曲线、召回率、F1值、混淆矩阵、受试者工作特征(ROC)曲线以及精确率-召回率(PR)曲线等方面评估了该模型的性能,并将深度学习模型与中医主治医师的诊断效率进行对比。结果训练结果显示,经过90个周期后,模型呈现出良好的分类效果,损失曲线和准确率趋于稳定,未见明显过拟合。模型在测试集上的准确率为92%,召回率为91%,F1分数为92%。混淆矩阵显示,模型的准确率为92%,中医师的准确率为69%。ROC和PR曲线下面积分别为0.97和0.95。结论由ResNet50构建的深度学习模型能有效识别染色舌苔图像,准确率优于中医师肉眼观察。它有望帮助医生识别假舌苔,避免误诊。