Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection

Reza Basiri, Milos R. Popovic, Shehroz S. Khan
11/27/2023
cs.CVcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{basiri2023domainspecific,
  title  = {Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection},
  author = {Reza Basiri and Milos R. Popovic and Shehroz S. Khan},
  year   = {2023},
  eprint = {2311.16312},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.16312v1}
}

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