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Skin disease diagnosis with deep learning: a review
Skin cancer is one of the most threatening diseases worldwide. However,
diagnosing skin cancer correctly is challenging. Recently, deep learning
algorithms have emerged to achieve excellent performance on various tasks.
Particularly, they have been applied to the skin disease diagnosis tasks. In
this paper, we present a review on deep learning methods and their applications
in skin disease diagnosis. We first present a brief introduction to skin
diseases and image acquisition methods in dermatology, and list several
publicly available skin datasets for training and testing algorithms. Then, we
introduce the conception of deep learning and review popular deep learning
architectures. Thereafter, popular deep learning frameworks facilitating the
implementation of deep learning algorithms and performance evaluation metrics
are presented. As an important part of this article, we then review the
literature involving deep learning methods for skin disease diagnosis from
several aspects according to the specific tasks. Additionally, we discuss the
challenges faced in the area and suggest possible future research directions.
The major purpose of this article is to provide a conceptual and systematically
review of the recent works on skin disease diagnosis with deep learning. Given
the popularity of deep learning, there remains great challenges in the area, as
well as opportunities that we can explore in the future