基于深度学习的糖尿病足伤口TEXAS分期研究

doi:10.3969/j.issn.0255-8297.2024.03.006

应用科学学报 ›› 2024, Vol. 42 ›› Issue (3): 437-446.doi: 10.3969/j.issn.0255-8297.2024.03.006

基于深度学习的糖尿病足伤口TEXAS分期研究

陈瑜倩¹, 吕东辉¹, 宋安平², 谢传涛²

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

收稿日期:2021-09-29 发布日期:2024-06-06
通信作者: 陈瑜倩，研究方向为计算机视觉与模式识别。E-mail: chenyuqian97@163.com E-mail:chenyuqian97@163.com

TEXAS Staging of Diabetic Foot Wounds Based on Deep Learning Approach

CHEN Yuqian¹, LYU Donghui¹, SONG Anping², XIE Chuantao²

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China;
2. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Received:2021-09-29 Published:2024-06-06

摘要/Abstract

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。

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

Abstract: 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.

Key words: TEXAS staging, ensemble convolutional neural network, transfer learning, diabetic foot, computer aided diagnosis

中图分类号:

P391.41

陈瑜倩, 吕东辉, 宋安平, 谢传涛. 基于深度学习的糖尿病足伤口TEXAS分期研究[J]. 应用科学学报, 2024, 42(3): 437-446.

CHEN Yuqian, LYU Donghui, SONG Anping, XIE Chuantao. TEXAS Staging of Diabetic Foot Wounds Based on Deep Learning Approach[J]. Journal of Applied Sciences, 2024, 42(3): 437-446.

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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

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