A benchmark for automatic visual classification of clinical skin disease images

Abstract

Skin disease is one of the most common human illnesses. It pervades all cultures, occurs at all ages, and affects between 30% and 70% of individuals, with even higher rates in at-risk. However, diagnosis of skin diseases by observing is a very difficult job for both doctors and patients, where an intelligent system can be helpful. In this paper, we mainly introduce a benchmark dataset for clinical skin diseases to address this problem. To the best of our knowledge, this dataset is currently the largest for visual recognition of skin diseases. It contains 6,584 images from 198 classes, varying according to scale, color, shape and structure. We hope that this benchmark dataset will encourage further research on visual skin disease classification. Moreover, the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks(CNNs), we also perform extensive analyses on this dataset using the state of the art methods including CNNs.