A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images
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
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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. Keywords: Skin disease image · Computer aided diagnosis · Image classification · CNNs · Hand-crafted features
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Introduction
Skin disease is one of the most common illnesses in human daily life. It pervades all cultures, occurs at all ages, and affects between 30 % and 70 % of individuals [1]. There are tens of millions of people affected by it every day. Skin disease is twofold, i.e. skin infection and skin neoplasm, in which thousands of skin conditions have been described [2]. Skin disease has a major adverse impact on quality of life and many are associated with significant psychosocial mobility. However, only a small proportion of people can recognize these diseases without access to a field guide. Moreover, there are many over-the-counter (OTC) drugs to treat the frequently-occurring skin diseases in daily life. In this case, correctly recognizing the skin diseases becomes very important for people who need to make a choice about these medicines. If people want to make a preliminary self diagnosis, it is undisputed that a visual recognition system will be useful for assisting them even if it is not perfect. For example, if an accurate skin disease classifier is developed, a user can submit a photo of recently skin condition to query a diagnosis. Surprisingly, there exists few research using computer vision techniques to recognize many common skin diseases based on ordinary photographical images. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VI, LNCS 9910, pp. 206–222, 2016. DOI: 10.1007/978-3-319-46466-4 13
A Benchmark for Classification of Clinical Skin Disease Images
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Fig. 1. Examples of dermoscopic and clinical images. (a) Dermoscopic images are acquired through a digital dermatoscope, which have relatively low levels of noise and consistent background illumination. (b) Clinical images are collected via various sources, most of which are captured with digital cameras and cell phones
Despite there are some related applications, the problem of recognizing skin diseases has not been full
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