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Diabetic foot ulcer classification systems use the presence of woundinfection (bacteria present within the wound) and ischaemia (restricted bloodsupply) as vital clinical indicators for treatment and prediction of woundhealing. Studies investigating the use of automated computerised methods ofclassifying infection and ischaemia within diabetic foot wounds are limited dueto a paucity of publicly available datasets and severe data imbalance in thosefew that exist. The Diabetic Foot Ulcer Challenge 2021 provided participantswith a more substantial dataset comprising a total of 15,683 diabetic footulcer patches, with 5,955 used for training, 5,734 used for testing and anadditional 3,994 unlabelled patches to promote the development ofsemi-supervised and weakly-supervised deep learning techniques. This paperprovides an evaluation of the methods used in the Diabetic Foot Ulcer Challenge2021, and summarises the results obtained from each network. The bestperforming network was an ensemble of the results of the top 3 models, with amacro-average F1-score of 0.6307.

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary