DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 38 Subclasses

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.

Automated Onychomycosis Detection Using Deep Neural Networks

Clinical dermatology, still relies heavily on manual introspection of fungi within a Potassium Hydroxide (KOH) solution using a brightfield microscope. However, this method takes a long time, is based on the experience of the clinician, and has a low accuracy. With the increase of neural network applications in the field of clinical microscopy it is now possible to automate such manual processes increasing both efficiency and accuracy. This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without colorants. Microscopic images of 81 fungi and 235 ceratine were collected. Then, smaller patches were extracted containing 2062 fungi and 2142 ceratine. In order to detect fungus and ceratine, two models were created one of which was a custom neural network and the other was based on the VGG16 architecture. The developed custom model had 99.84% accuracy, and an area under the curve (AUC) value of 1.00, while the VGG16 model had 98.89% accuracy and an AUC value of 0.99. However, average accuracy and AUC value of clinicians is 72.8% and 0.87 respectively. This deep learning model allows the development of an automated system that can detect fungi within microscopic images.