Co2Wounds-V2: Extended Chronic Wounds Dataset from Leprosy Patients

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.

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Published
2024-09-27
DOI
https://doi.org/10.1109/icip51287.2024.10647641
Citations
1
Primary Topic
Leprosy Research and Treatment
Type
article
Field-Weighted Citation Impact
0.39
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Co2Wounds-V2: Extended Chronic Wounds Dataset from Leprosy Patients

Karen Sánchez, Bernard Ghanem, Carlos Hinojosa, Henry Argüello et al.
1 citations
Leprosy Research and Treatment
0.39
article

Co2Wounds-V2: Extended Chronic Wounds Dataset from Leprosy Patients

Karen Sánchez, Bernard Ghanem, Carlos Hinojosa, Henry Argüello, Olinto Mieles, Chen Zhao
article en
1 citations

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.

Universidad Industrial de Santander (CO), King Abdullah University of Science and Technology (SA)
Zero hunger
Openalex Percentile: Top 36%
Leprosy Research and Treatment
0.39
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