Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and
evaluations for treatment. DFU patient population is on the rise and will soon
outpace the available health resources. Autonomous monitoring and evaluation of
DFU wounds is a much-needed area in health care. In this paper, we evaluate and
identify the most accurate feature extractor that is the core basis for
developing a deep-learning wound detection network. For the evaluation, we used
mAP and F1-score on the publicly available DFU2020 dataset. A combination of
UNet and EfficientNetb3 feature extractor resulted in the best evaluation among
the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier
in the development of a comprehensive DFU domain-specific autonomous wound
detection pipeline.

自主监测和评估糖尿病足溃疡（DFU）是卫生保健中一个亟需关注的领域，本研究评估并确定用于开发深度学习伤口检测网络的最准确的特征提取器，并发现 UNet 和 Efficientnetb3 结合的特征提取器在 14 个网络中表现最佳，可作为糖尿病足溃疡领域特定自主伤口检测流程的分类器。