Abstract
Skin diseases have a significant impact on the socio-economic landscape as they affect not only the medical health of the patient but also their psychological well-being. Moreover, as the majority of individuals suffering from skin diseases are over the age of 60, these individuals have to also cope with the stress associated to age-related conditions such as diabetes, high blood pressure, and cardiac diseases. To alleviate this burden, it is essential to identify skin diseases at an early stage, which can help prevent disease progression. With the advent of Artificial Intelligence (AI) and technology, the use of automated disease diagnosis systems has increased significantly. These systems assist medical specialists by reducing diagnosis time and accelerating the entire diagnostic process. However, deep learning models require substantial amounts of data for training. In histopathology, brightfield microscopy is the most widely used imaging modality for identifying diseases through the examination of underlying structures. We are publishing a dataset comprising 38 whole-slide Hematoxylin and Eosin-stained images along with their masks. These images were grouped into 12 classes including tissues, skin cancer, and skin layers. We have also validated the dataset using SegFormer, which resulted in an overall accuracy of 0.875.