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CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients

Image and Video Processing 2024-08-21 v1 Computer Vision and Pattern Recognition

Abstract

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.

Keywords

Cite

@article{arxiv.2408.10827,
  title  = {CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients},
  author = {Karen Sanchez and Carlos Hinojosa and Olinto Mieles and Chen Zhao and Bernard Ghanem and Henry Arguello},
  journal= {arXiv preprint arXiv:2408.10827},
  year   = {2024}
}

Comments

2024 IEEE International Conference on Image Processing (ICIP 2024)