The DFUC 2020 dataset: Analysis towards diabetic foot ulcer detection

Bill Cassidy, Neil D. Reeves, Joseph M. Pappachan, David Gillespie, Claire O'Shea, Satyan Rajbhandari, Arun G. Maiya, Eibe Frank, Andrew J.M. Boulton, David G. Armstrong, Bijan Najafi, Justina Wu, Rupinder Singh Kochhar, Moi Hoon Yap

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns the automated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovative healthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app that patients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance of a DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHS Foundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research.

Original languageEnglish
Pages (from-to)5-11
Number of pages7
JournalEuropean Endocrinology
Issue number1
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology
  • Endocrine and Autonomic Systems


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