Deep Learning and Machine Learning models for Skin Disease Detection.A Systematic Review

For the early treatment of specific dermatological criteria, skin disease detection is crucial. Due to advances in DL techniques and computer vision, learning-based approaches to skin disease analysis have recently received a lot of interest. This survey study provides an overview of the methodologies used for segmentation, classification, and detection of dermatological conditions. This comprehensive review examines the difficulties associated with physical examination and emphasizes the importance of assessing skin disease in healthcare. A comprehensive study of cutting-edge studies focused on the categorization and detection of skin diseases is presented to reliably identify the skin disease type using macroscopic, dermoscopic, and additional imaging formats. This study article examines the deep learning techniues and architectures and classic ML algorithms, analyzing their strengths and limitations. The study analyzes research publications on skin disease segmentation and detection methods to determine the accurate boundaries of skin diseases and classify them accurately. These methods facilitate the execution of further studies and allows accurate measurements and quantitative assessments. The survey study covers popular segmentation techniques, such as those based on regions, graphs, and deep learning. There is an additional discussion of the challenges, datasets, and performance metrics specific to skin disease segmentation.