数字中医药（英文）2021，Vol.4Issue(2)：92-101,10.DOI:10.1016/j.dcmed.2021.06.003
基于深度残差网络的银屑病分类诊断模型研究
Research on classification diagnosis model of psoriasis based on deep residual network
摘要
Abstract
Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper. Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors, simplify the diagnosis and treatment process, and improve the quality of diagnosis. Methods Firstly, data enhancement, image resizings, and TFRecord coding are used to preprocess the input of the model, and then a 34-layer deep residual network (ResNet-34) is constructed to extract the characteristics of psoriasis. Finally, we used the Adam algorithm as the optimizer to train ResNet-34, used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model, and obtained an optimized ResNet-34 model for psoriasis diagnosis. Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate, F1-score and ROC curve. Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis, and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.关键词
银屑病/深度残差网络/数据增强/交叉熵/Adam算法/召回率Key words
Psoriasis/Deep residual network/Data enhancement/Cross-entropy/Adam algorithm/Recall引用本文复制引用
李鹏,伊娜,丁长松,李晟,闵慧..基于深度残差网络的银屑病分类诊断模型研究[J].数字中医药（英文）,2021,4(2):92-101,10.基金项目
We thank for the funding support from the Key Research and Development Plan of China(No.2017YFC1703306),Youth Project of Natural Science Foundation of Hunan Province(No.2019JJ50453),Project of Hunan Health Commission(No.202112072217),Open Fund Project of Hunan University of Traditional Chinese Medicine(No.2018JK02),and General Project of Education Department of Hunan Province(No.19C1318). （No.2017YFC1703306）
