Abstract:

One of the primary problems in our society is skin diseases, due to their severe effects on both the body and the mental health of individuals. Early detection and diagnosis of these diseases can have a significant impact on the success of treatments. The skills and experience of specialists play a crucial role in determining effective methods for diagnosing and treating skin lesions. In the early stages, advanced techniques were developed to automatically test skin for diseases. In recent years, skin diseases have increasingly been diagnosed with the help of artificial intelligence through machine learning algorithms, which are trained on vast amounts of data already available in the healthcare sector. In this research report, we comprehensively investigated previous studies on the use of machine learning in skin disease classification. Skin diseases were successfully classified in several studies with varying levels of diagnostic accuracy. Some studies used image processing and feature extraction for this purpose, while others focused on specific types of skin diseases by utilizing clinical features obtained from tissue analyses of the affected areas. This research report concludes that using image processing methods, accuracy ranged between 50% and 100%. Another method, involving tissue analysis, achieved an excellent accuracy rate of 94% or higher. The findings present a review of the related studies conducted in the literature and focus on the recent research gaps.