Development of Diabetic Foot Ulcer Datasets: An Overview
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Abstract (arXiv)
This paper provides conceptual foundation and procedures used in the development of diabetic foot ulcer datasets over the past decade, with a timeline to demonstrate progress. We conduct a survey on data capturing methods for foot photographs, an overview of research in developing private and public datasets, the related computer vision tasks (detection, segmentation and classification), the diabetic foot ulcer challenges and the future direction of the development of the datasets. We report the distribution of dataset users by country and year. Our aim is to share the technical challenges that we encountered together with good practices in dataset development, and provide motivation for other researchers to participate in data sharing in this domain.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 46 references that are in the map. First posted on arXiv 1 Jan 2022.
Builds on
8 of 46 references.
References · 8
- 2020Fully Automatic Wound Segmentation with Deep Convolutional Neural Networkscited by 32
- 2019Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniquescited by 19
- 2020Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluationcited by 19
- 2017Fully Convolutional Networks for Diabetic Foot Ulcer Segmentationcited by 17
- 2017DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classificationcited by 16
- 2021Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcerscited by 16
- 2021Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summarycited by 8
- 2021A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcerscited by 7
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.