DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses
Abstract Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04104-3 |
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| _version_ | 1850056181238726656 |
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| author | Abdurrahim Yilmaz Sirin Pekcan Yasar Gulsum Gencoglan Burak Temelkuran |
| author_facet | Abdurrahim Yilmaz Sirin Pekcan Yasar Gulsum Gencoglan Burak Temelkuran |
| author_sort | Abdurrahim Yilmaz |
| collection | DOAJ |
| description | Abstract Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images. |
| format | Article |
| id | doaj-art-5519e1aed03345b6a4023c5e00aecb61 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-5519e1aed03345b6a4023c5e00aecb612025-08-20T02:51:46ZengNature PortfolioScientific Data2052-44632024-11-0111111210.1038/s41597-024-04104-3DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 SubclassesAbdurrahim Yilmaz0Sirin Pekcan Yasar1Gulsum Gencoglan2Burak Temelkuran3Imperial College London, Division of Systems Medicine, Department of Metabolism, Digestion, and ReproductionThe University of Health Sciences, Haydarpasa Numune Research and Training Hospital, Department of Dermatology and VenereologyIstinye University, Liv Hospital Vadistanbul, Department of Dermatology and VenereologyImperial College London, Division of Systems Medicine, Department of Metabolism, Digestion, and ReproductionAbstract Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images.https://doi.org/10.1038/s41597-024-04104-3 |
| spellingShingle | Abdurrahim Yilmaz Sirin Pekcan Yasar Gulsum Gencoglan Burak Temelkuran DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses Scientific Data |
| title | DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses |
| title_full | DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses |
| title_fullStr | DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses |
| title_full_unstemmed | DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses |
| title_short | DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses |
| title_sort | derm12345 a large multisource dermatoscopic skin lesion dataset with 40 subclasses |
| url | https://doi.org/10.1038/s41597-024-04104-3 |
| work_keys_str_mv | AT abdurrahimyilmaz derm12345alargemultisourcedermatoscopicskinlesiondatasetwith40subclasses AT sirinpekcanyasar derm12345alargemultisourcedermatoscopicskinlesiondatasetwith40subclasses AT gulsumgencoglan derm12345alargemultisourcedermatoscopicskinlesiondatasetwith40subclasses AT buraktemelkuran derm12345alargemultisourcedermatoscopicskinlesiondatasetwith40subclasses |