基于深度学习的糖尿病足伤口TEXAS分期研究  

TEXAS Staging of Diabetic Foot Wounds Based on Deep Learning Approach

作　　者：陈瑜倩 吕东辉[1] 宋安平[2] 谢传涛 CHEN Yuqian;LYU Donghui;SONG Anping;XIE Chuantao(School of Communication and Information Engineering,Shanghai University,Shanghai 200444,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学通信与信息工程学院,上海200444 [2]上海大学计算机工程与科学学院,上海200444

出　　处：《应用科学学报》2024年第3期437-446,共10页Journal of Applied Sciences

摘　　要：针对糖尿病足辅助诊断问题,提出了一种有效的具有两级集成卷积神经网络的深度学习方法。利用加载预训练权重的121层密集卷积网络DenseNet121和EfficientNet-B0网络作为集成卷积神经网络训练时特征提取的初始参数;再使用数据集Diabetic Foot UlcersGrand Challenge 2021进行整个网络的训练,从而实现糖尿病足伤口感染和缺血特征的TEXAS自动分期。使用5折交叉验证获得的该方法受试者工作特征曲线下面积值为0.989,准确率为0.954,查全率为0.944,查准率为0.954,F1-score为0.956。结果显示该方法性能良好,在临床辅助诊断中具有较好的应用潜力。In order to solve the problem of diabetic foot auxiliary diagnosis,an efficient deep learning method with two-level ensemble convolutional neural network was proposed.This paper proposes an efficient deep learning method featured with two-level ensemble convolutional neural networks.The approach utilizes DenseNet121 and EfficientNet-B0 networks with pre-training weight as initial parameters for feature extraction during network training.The Diabetic Foot Ulcers Grand Challenge 2021 dataset is used to train the parameters of whole network,so as to realize the automatic staging of diabetic foot in terms of wound infection and ischemia.5-fold cross-validation was used to verify the proposed trained network.The proposed method achieves high accuracy,with AUC(area under the receiver operating characteristic curve)value,accuracy,recall,precision,and F1-score of the network measured as 0.989,0.954,0.944,0.954,0.956,respectively.The method demonstrates promising potential for assisting the staging of diabetic foot in clinical.

关 键 词：TEXAS分期 集成卷积神经网络 迁移学习 糖尿病足 计算机辅助诊断 

分 类 号：P391.41[天文地球—地球物理学]