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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.10827 |
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| _version_ | 1866917754206945280 |
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| author | Sanchez, Karen Hinojosa, Carlos Mieles, Olinto Zhao, Chen Ghanem, Bernard Arguello, Henry |
| author_facet | Sanchez, Karen Hinojosa, Carlos Mieles, Olinto Zhao, Chen Ghanem, Bernard Arguello, Henry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients Sanchez, Karen Hinojosa, Carlos Mieles, Olinto Zhao, Chen Ghanem, Bernard Arguello, Henry Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.10827 |