Robust Skin Disease Diagnosis with Deep Belief Network

Abstract

The skin is one of the first lines of defenceagainst environmental influences such as sunlight, bacteriaand germs, which cause various skin diseases. In addition to the bad psychological and physical impact caused by skin disease. So,in recent years,many artificial intelligence (AI) algorithms have appeared that canrecognize Images, through which skin diseases can be diagnosed, avoiding traditional methods that rely on visual examination and self-evaluation based on experience. The paper aims to classify a group of skin diseases according to the type of disease, such as Atopic Dermatitis, Dyshidrotic Eczema and Nummular Dermatitisusing a deep belief network algorithm(DBN) thatwas built to suit the work. A global dataset was also used, obtained from the Kaggle website, and after conducting experiments on it, the algorithm achieved high accuracy in diagnosing diseases, the percentage reached 98.773%. It is possible in the future to use the network to classify other types of diseases after providing it with a large number of images of affected people