  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.
