# Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques

arXiv: https://arxiv.org/abs/1908.05317 · HTML: https://mlsanity.com/paper/1908.05317/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021)
- [Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network](https://mlsanity.com/paper/2109.06969/) (2021)
- [Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach](https://mlsanity.com/paper/2308.11877/) (2023)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (2021)
- [Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2111.06894/) (2021)
- [HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2209.07313/) (2022)
- [Development of Diabetic Foot Ulcer Datasets: An Overview](https://mlsanity.com/paper/2201.00163/) (2022)
- [Wound Severity Classification using Deep Neural Network](https://mlsanity.com/paper/2204.07942/) (2022)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Explainable, Multi-modal Wound Infection Classification from Images Augmented with Generated Captions](https://mlsanity.com/paper/2502.20277/) (2025)
- [Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization](https://mlsanity.com/paper/2504.17628/) (2025)
- [Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer](https://mlsanity.com/paper/2110.01795/) (2021)
- [Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques](https://mlsanity.com/paper/2209.09880/) (2022)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)
- [Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement](https://mlsanity.com/paper/2305.01044/) (2023)
- [Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers](https://mlsanity.com/paper/2405.00858/) (2024)
- [Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation](https://mlsanity.com/paper/2603.00459/) (2026)
- [Attention-based Pin Site Image Classification in Orthopaedic Patients with External Fixators](https://mlsanity.com/paper/2603.24815/) (2026)
- [In-Context Learning for Wound Classification with Small Multimodal Language Models](https://mlsanity.com/paper/2607.18819/) (2026)

## Abstract (arXiv)

Recognition and analysis of Diabetic Foot Ulcers (DFU) using computerized methods is an emerging research area with the evolution of image-based machine learning algorithms. Existing research using visual computerized methods mainly focuses on recognition, detection, and segmentation of the visual appearance of the DFU as well as tissue classification. According to DFU medical classification systems, the presence of infection (bacteria in the wound) and ischaemia (inadequate blood supply) has important clinical implications for DFU assessment, which are used to predict the risk of amputation. In this work, we propose a new dataset and computer vision techniques to identify the presence of infection and ischaemia in DFU. This is the first time a DFU dataset with ground truth labels of ischaemia and infection cases is introduced for research purposes. For the handcrafted machine learning approach, we propose a new feature descriptor, namely the Superpixel Color Descriptor. Then we use the Ensemble Convolutional Neural Network (CNN) model for more effective recognition of ischaemia and infection. We propose to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Finally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus non-infection. Overall, our method performed better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.