[1]朱倩倩,王澜,姜楠,等.基于深度学习的痰湿体质高血压舌象识别[J].中国医学物理学杂志,2025,42(4):534-541.[doi:10.3969/j.issn.1005-202X.2025.04.016]
　ZHU Qianqian,WANG Lan,JIANG Nan,et al.Deep learning-based tongue image recognition for hypertension with phlegm-dampnessconstitution[J].Chinese Journal of Medical Physics,2025,42(4):534-541.[doi:10.3969/j.issn.1005-202X.2025.04.016]
点击复制

基于深度学习的痰湿体质高血压舌象识别()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第4期
页码:
534-541
栏目:
医学人工智能
出版日期:
2025-04-20

文章信息/Info

Title:
Deep learning-based tongue image recognition for hypertension with phlegm-dampnessconstitution
文章编号:
1005-202X（2025）04-0534-08
作者:
朱倩倩 1; 王澜 1; 姜楠 1; 董昌武 2
1.安徽中医药大学中医学院，安徽 合肥 230012；2.安徽中医药大学第二附属医院名医堂，安徽 合肥 230061
Author(s):
ZHU Qianqian1;  WANG Lan1;  JIANG Nan1;  DONG Changwu2
1. College of Chinese Medicine, Anhui University of Chinese Medicine, Hefei 230012, China; 2. Eminent Physician Clinic, The SecondAffiliated Hospital of Anhui University of Chinese Medicine, Hefei 230061, China
关键词:
舌象; 深度学习; 高血压; 痰湿体质; 神经网络
Keywords:
tongue manifestation;  deep learning;  hypertension;  phlegm-dampness constitution;  neural network
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2025.04.016
文献标志码:
A
摘要:
目的：使用深度学习语义分割模型及残差神经网络模型对痰湿体质人群是否患有高血压病进行舌象的客观化识别分析，推动舌象研究的现代化进程，为中医临床决策提供更为客观、科学的依据。方法：首先使用LabelMe图像标签标注工具对547例受试者的舌象舌体区域进行划分标注，再使用U-Net分割算法进行舌体分割，将舌体从复杂的背景中单独分离出以便后续的分析。后续研究引入了ResNet-34、ResNet-50以及YOLOv5 3种深度学习模型，将痰湿体质高血压患者舌象和痰湿体质亚健康者舌象进行分类，构建分类模型，绘制混淆矩阵并计算F1值、准确率等对分类模型效果进行评价。结果：实验结果显示，3种模型在本次分类任务中均有较好的表现。ResNet-34模型F1值为91.46%，准确率为92.87%，精准率为90.48%，召回率为92.89%。ResNet-50模型总体上更优，F1值为92.08%，准确率为93.05%，精准率为95.26%，召回率为89.11%。YOLOv5模型总体准确率为85.6%，在痰湿体质高血压患者和痰湿体质亚健康者两个类别上，分别取得了85.3% 和 85.7% 的准确率。结论：ResNet-34、ResNet-50、YOLOv5 在本次分类任务中均表现优异，以 ResNet-50 最佳。证明了深度学习模型可以较好地完成舌象的分类识别任务，体现了深度学习技术在中医舌诊自动化分类中的巨大潜力，也为中医诊断的现代化、客观化提供了有力的技术支撑。
Abstract:
Objective To objectively identify whether people with phlegm-dampness constitution suffer from hypertension ornot using deep learning semantic segmentation model and residual neural network, so as to promote the modernization oftongue manifestation research, and provide a more objective and scientific basis for clinical decision-making in traditionalChinese medicine (TCM). Methods The tongue regions of 547 subjects were outlined and labeled using the Label Me imagelabeling tool, followed by tongue body segmentation using the U-Net segmentation algorithm which separated the tonguebody from the complex background. In the subsequent study, 3 deep learning models, namely ResNet-34, ResNet-50 andYOLOv5, were used to classify the tongue manifestations of hypertensive patients and the sub-health both with phlegmdampness, and to construct the corresponding classification models whose performances were objectively evaluated bydrawing confusion matrix and calculating F1 value and accuracy. Results The experimental results showed that all 3 modelsperformed well in the classification task. ResNet-34 vs ResNet-50 had F1 values of 91.46% vs 92.08%, accuracies of 92.87%vs 93.05%, precisions of 90.48% vs 95.26%, and recall rates of 92.89% vs 89.11%. YOLOv5 had an overall accuracy of85.6%, achieving 85.3% and 85.7% accuracies in the specific classifications for hypertensive patients with phlegm-dampnessand the sub-health with phlegm-dampness. Conclusion All 3 models (ResNet-34, ResNet-50 and YOLOv5) performed wellin the classification task, with ResNet-50 being the best. It proves that the deep learning model can better accomplish the classification and recognition of tongue manifestations, which reflects the great potential of deep learning in the automatedclassification for TCM tongue diagnosis, and also provides a strong technical support for the modernization and objectivity ofTCM diagnosis.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(4):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
　MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(4):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[3]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(4):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[4]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(4):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
　AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(4):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[7]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(4):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
　ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(4):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[9]温佳圆,林国钰,张逸文,等.应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割[J].中国医学物理学杂志,2020,37(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
　WEN Jiayuan,LIN Guoyu,ZHANG Yiwen,et al.Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network[J].Chinese Journal of Medical Physics,2020,37(4):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[10]秦楠楠,薛旭东,吴爱林,等.基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J].中国医学物理学杂志,2020,37(4):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
　QIN Nannan,XUE Xudong,WU Ailin,et al.Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network[J].Chinese Journal of Medical Physics,2020,37(4):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]

备注/Memo

备注/Memo:
【收稿日期】2024-11-20【基金项目】国家自然科学基金（82174277）；安徽省中医药科技攻关专项（202303a07020008）；安徽省重点研究与开发计划项目（2022h11020018）；安徽省临床医学研究转化专项（202304295107020107）；新安医学与中医药现代化研究所“揭榜挂帅”项目（2024CXMMTCM002）；董昌武安徽省名医工作室项目（皖中医药发展秘〔2024〕19号）【作者简介】朱倩倩，硕士研究生，研究方向：四诊客观化及心脑血管疾病中医证候，E-mail: qq1127666@163.com【通信作者】董昌武，教授，博士生导师，研究方向：四诊客观化及心脑血管疾病中医证候，E-mail: dcw1018@aliyun.com
更新日期/Last Update: 2025-04-30