Skin Diseases have become one of the most frequent diseases in the world; apart from their physical effects, they are rapidly expanding to cover a bigger region and have a psychological impact on patients. Diagnosing skin diseases takes a high degree of knowledge and is objective to the dermatologist. Presented an overview of Deep-Learning algorithms and their usage in skin disease diagnostics. Begin with a short overview of skin diseases and image collecting techniques in dermatology, followed by a list of publicly accessible Skin-Datasets for algorithm training and testing are discussed.  Next examine the research including deep learning algorithms for skin disease detection from many perspectives according to the particular objectives as an essential element of this paper. The main objective of this paper is to present a comprehensive and simplify analysis of contemporary deep learning-based skin disease diagnostic research. By considering the growing popularity of deep learning, there are still several challenges to overcome as well as chances to explore in the future. This study found that the diagnostic accuracy of image processing techniques was inconsistent, but the accuracy of deep Learning and Machine Learning algorithms was high, with a 99.04 % accuracy rate.

Owing to its costly diagnosis, which requires elevated human interpretation, skin conditions are one of the major medical problems. In assessing the chance of getting treated, early-stage diagnosis of skin disorders plays a crucial role. We assume that the use of an accurate classifier helps to diagnose early-stage skin disorders. Therefore, we present a fully automated classification system in this article for diagnosing skin disorders by using image mining techniques. Our model is designed to compromise pixel, object and pattern levels in three steps, respectively. We have used multiple image mining techniques like augmentation, feature extraction, classification in order to classify skin disorder images effectively.