Imperial College London, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, London, SW7 2AZ, United Kingdom.
The University of Health Sciences, Haydarpasa Numune Research and Training Hospital, Department of Dermatology and Venereology, Istanbul, 34668, Turkey.
Sci Data. 2024 Nov 28;11(1):1302. doi: 10.1038/s41597-024-04104-3.
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.
皮肤损伤数据集为理解各种皮肤状况和开发有效的诊断工具提供了重要信息。它们有助于基于人工智能的早期皮肤癌检测，方便治疗计划，并有助于医学教育和研究。已发表的大型数据集部分涵盖了皮肤损伤的亚类。这一局限性突出表明需要更广泛和多样化的数据集，以减少错误预测，并有助于改进皮肤损伤的故障分析。本研究提出了一个包含 12345 张皮肤镜图像的多样化数据集，涵盖了来自土耳其的 40 种皮肤损伤亚类，这些图像涵盖了欧洲和亚洲过渡区的不同皮肤类型。每个亚组都包含高分辨率图像和专家注释，为未来的研究提供了强大而可靠的基础。本研究对每个亚组的详细分析有助于有针对性的研究工作，并加深对皮肤损伤的理解。该数据集通过其 5 个超级类、15 个主要类、40 个子类和 12345 张高分辨率皮肤镜图像的多样化结构脱颖而出。
