Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models.
Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models.
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DOI：
10.1109/ojemb.2022.3219725
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发表时间：
2022
影响因子：
5.8
通讯作者：
中科院分区：
文献类型：
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动机：糖尿病足溃疡（DFUs）的感染（伤口细菌）和缺血（血液供应不足）增加了截肢的风险。目的：开发一种基于图像的DFU感染和缺血检测系统。方法：使用几何和彩色图像操作增强DFU数据集，然后使用effentnet深度学习模型和一组综合基线进行二元感染和缺血分类。结果：effentnets模型在缺血分类中达到99%的准确率，在感染分类中达到98%的准确率，优于ResNet和Inception（87%的准确率）以及Ensemble CNN，后者是目前最先进的技术（缺血分类准确率为90%，感染分类准确率为73%）。EfficientNets还在基线模型所用时间的一小部分（10%到50%）内对测试图像进行了分类。结论：本研究表明，EfficientNets是一种可行的感染和缺血分类深度学习模型。
Motivation: Infection (bacteria in the wound) and ischemia (insufficient blood supply) in Diabetic Foot Ulcers (DFUs) increase the risk of limb amputation. Goal: To develop an image-based DFU infection and ischemia detection system that uses deep learning. Methods: The DFU dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model and a comprehensive set of baselines. Results: The EfficientNets model achieved 99% accuracy in ischemia classification and 98% in infection classification, outperforming ResNet and Inception (87% accuracy) and Ensemble CNN, the prior state of the art (Classification accuracy of 90% for ischemia 73% for infection). EfficientNets also classified test images in a fraction (10% to 50%) of the time taken by baseline models. Conclusions: This work demonstrates that EfficientNets is a viable deep learning model for infection and ischemia classification.