Bill Cassidy1, Connah Kendrick1, Neil D. Reeves2, Joseph M. Pappachan3,Claire O’Shea4, David G. Armstrong5 , and Moi Hoon Yap1
Abstract. 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 di- abetic 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 substan- tial 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 eval- uation of the methods used in the Diabetic Foot Ulcer Challenge 2021, and summarises the results obtained from each network. The best per- forming network was an ensemble of the results of the top 3 models, with a macro-average F1-score of 0.6307.