  This paper introduces the Diabetic Foot Ulcers dataset (DFUC2021) for
analysis of pathology, focusing on infection and ischaemia. We describe the
data preparation of DFUC2021 for ground truth annotation, data curation and
data analysis. The final release of DFUC2021 consists of 15,683 DFU patches,
with 5,955 training, 5,734 for testing and 3,994 unlabeled DFU patches. The
ground truth labels are four classes, i.e. control, infection, ischaemia and
both conditions. We curate the dataset using image hashing techniques and
analyse the separability using UMAP projection. We benchmark the performance of
five key backbones of deep learning, i.e. VGG16, ResNet101, InceptionV3,
DenseNet121 and EfficientNet on DFUC2021. We report the optimised results of
these key backbones with different strategies. Based on our observations, we
conclude that EfficientNetB0 with data augmentation and transfer learning
provided the best results for multi-class (4-class) classification with
macro-average Precision, Recall and F1-score of 0.57, 0.62 and 0.55,
respectively. In ischaemia and infection recognition, when trained on
one-versus-all, EfficientNetB0 achieved comparable results with the state of
the art. Finally, we interpret the results with statistical analysis and
Grad-CAM visualisation.
