Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
This work addresses the need for autonomous monitoring of diabetic foot ulcers, which is crucial for healthcare as patient numbers rise, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of diabetic foot ulcer detection by evaluating 14 deep learning networks, finding that a combination of UNet and EfficientNetb3 achieved the best performance on the DFU2020 dataset using mAP and F1-score metrics.
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.