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