  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 38 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 photos 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 5 super
classes, 15 main classes, 38 subclasses and its 12,345 high-resolution
dermatoscopic images.
