Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers

TL;DR

This paper presents a new dataset for diabetic foot ulcer analysis, benchmarks deep learning models for classifying infection and ischaemia, and identifies EfficientNetB0 as the most effective approach.

Contribution

Introduction of the DFUC2021 dataset, comprehensive benchmarking of deep learning models, and analysis of classification performance for diabetic foot ulcer pathology.

Findings

EfficientNetB0 with data augmentation achieved best multi-class classification results.

The dataset contains 15,683 patches with detailed annotations.

Grad-CAM visualizations aid in interpreting model decisions.

Abstract

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.