Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

Comput Biol Med. 2021 Aug;135:104596. doi: 10.1016/j.compbiomed.2021.104596. Epub 2021 Jun 23.


There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

Keywords: DFUC2020; Deep learning; Diabetic foot ulcers; Machine learning; Object detection.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Deep Learning*
  • Diabetes Mellitus*
  • Diabetic Foot* / diagnosis
  • Humans
  • Research Design