Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brüngel, Bill Cassidy, Manu Goyal, Hongtao Zhu, Johannes Rückert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M. Friedrich, David B. Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D. Reeves, Joseph M. Pappachan, Claire O'SheaEibe Frank

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


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.

Original languageEnglish
Article number104596
JournalComputers in Biology and Medicine
Publication statusPublished - 2021 Aug


  • DFUC2020
  • Deep learning
  • Diabetic foot ulcers
  • Machine learning
  • Object detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


Dive into the research topics of 'Deep learning in diabetic foot ulcers detection: A comprehensive evaluation'. Together they form a unique fingerprint.

Cite this