Wang Cheng, Yu Zhen, Long Zhou, Zhao Hui, Wang Zhenwei
Shandong Academy of Intelligent Computing Technology, Shandong Institutes of Industrial Technology (SDIIT), Jinan, 250000, China.
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology (ICT), Beijing, China.
Sci Rep. 2024 Dec 2;14(1):29877. doi: 10.1038/s41598-024-80691-w.
Diabetes foot ulcer (DFU) is one of the common complications of diabetes patients, which may lead to infection, necrosis and even amputation. Therefore, early diagnosis, classification of severity and related treatment are crucial for the patients. Current DFU classification methods often require experienced doctors to manually classify the severity, which is time-consuming and low accuracy. The objective of the study is to propose a few-shot DFU image classification method based on deep residual neural network and transfer learning. Considering the difficulty in obtaining clinical DFU images, it is a few-shot problem. Therefore, the methods include: (1) Data augmentation of the original DFU images by using geometric transformations and random noise; (2) Deep ResNet models selection based on different convolutional layers comparative experiments; (3) DFU classification model training with transfer learning by using the selected pre-trained ResNet model and fine tuning. To verify the proposed classification method, the experiments were performed with the original and augmented datasets by separating three classifications: zero grade, mild grade, severe grade. (1) The datasets were augmented from the original 146 to 3000 image datasets and the corresponding DFU classification's average accuracy from 0.9167 to 0.9867; (2) Comparative experiments were conducted with ResNet18, ResNet34, ResNet50, ResNet101, ResNet152 by using 3000 image datasets, and the average accuracy/loss is 0.9325/0.2927, 0.9276/0.3234, 0.9901/0.1356, 0.9865/0.1427, 0.9790/0.1583 respectively; (3) Based on the augmented 3000 image datasets, it was achieved 0.9867 average accuracy with the DFU classification model, which was trained by the pre-trained ResNet50 and hyper-parameters. The experimental results indicated that the proposed few-shot DFU image classification method based on deep ResNet and transfer learning got very high accuracy, and it is expected to be suitable for low-cost and low-computational terminal equipment, aiming at helping clinical DFU classification efficiently and auxiliary diagnosis.
糖尿病足溃疡（DFU）是糖尿病患者常见的并发症之一，可能导致感染、坏死甚至截肢。因此，早期诊断、严重程度分级及相关治疗对患者至关重要。当前的DFU分级方法通常需要经验丰富的医生手动进行严重程度分级，这既耗时又准确率低。本研究的目的是提出一种基于深度残差神经网络和迁移学习的少样本DFU图像分类方法。考虑到获取临床DFU图像的困难，这是一个少样本问题。因此，方法包括：（1）通过几何变换和随机噪声对原始DFU图像进行数据增强；（2）基于不同卷积层的对比实验选择深度ResNet模型；（3）使用选定的预训练ResNet模型并通过微调进行迁移学习训练DFU分类模型。为验证所提出的分类方法，通过将零级、轻度、重度三种分类分开，对原始数据集和增强数据集进行了实验。（1）数据集从原始的146个图像数据集增加到3000个，相应的DFU分类平均准确率从0.9167提高到0.9867；（2）使用3000个图像数据集对ResNet18、ResNet34、ResNet50、ResNet101、ResNet152进行对比实验，平均准确率/损失分别为0.9325/0.2927、0.9276/0.3234、0.9901/0.1356、0.9865/0.1427、0.9790/0.1583；（3）基于增强后的3000个图像数据集，使用预训练的ResNet50和超参数训练的DFU分类模型平均准确率达到0.9867。实验结果表明，所提出的基于深度ResNet和迁移学习的少样本DFU图像分类方法具有很高的准确率，有望适用于低成本、低计算量的终端设备，旨在高效辅助临床DFU分类和辅助诊断。
