  Diabetic foot ulcer classification systems use the presence of wound
infection (bacteria present within the wound) and ischaemia (restricted blood
supply) as vital clinical indicators for treatment and prediction of wound
healing. Studies investigating the use of automated computerised methods of
classifying infection and ischaemia within diabetic foot wounds are limited due
to a paucity of publicly available datasets and severe data imbalance in those
few that exist. The Diabetic Foot Ulcer Challenge 2021 provided participants
with a more substantial dataset comprising a total of 15,683 diabetic foot
ulcer patches, with 5,955 used for training, 5,734 used for testing and an
additional 3,994 unlabelled patches to promote the development of
semi-supervised and weakly-supervised deep learning techniques. This paper
provides an evaluation of the methods used in the Diabetic Foot Ulcer Challenge
2021, and summarises the results obtained from each network. The best
performing network was an ensemble of the results of the top 3 models, with a
macro-average F1-score of 0.6307.
