CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients
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Abstract (arXiv)
Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation. However, the effectiveness of these algorithms heavily depends on the availability of comprehensive and varied wound image data, which is usually scarce. This paper introduces the CO2Wounds-V2 dataset, an extended collection of RGB wound images from leprosy patients with their corresponding semantic segmentation annotations, aiming to enhance the development and testing of image-processing algorithms in the medical field.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 20 Aug 2024.
Builds on
6 of 25 references.
References · 6
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- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
- 2019Panoptic Feature Pyramid Networkscited by 645
- 2017Fully Convolutional Networks for Diabetic Foot Ulcer Segmentationcited by 17
- 2023Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinomacited by 1
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Citing papers per year: 2026: 2.
2 papers.