STATE-OF-THE-ART IN SKIN DISEASE RECOGNITION AND CLASSIFICATION: AN IN-DEPTH ANALYSIS OF HYBRID DEEP LEARNING MODELS AND OPTIMIZATION TECHNIQUES

Research on computer-aided systems for skin lesion diagnosis is expanding. Researchers' interest in creating computer-aided diagnosis systems has grown recently. A visual examination is a useful tool for detecting skin tumors, and dermoscopic research and other investigations can confirm the diagnosis. This is so that distinct skin pictures can be identified early on by eye inspection. In the realm of medical procedures, skin disorders are among the most prevalent, and when opposed to other condition kinds, they are more visibly represented. Thus, the application of deep learning strategies for skin conditions Investigators are interested in the identification of images because of their importance. The use of deep learning techniques for illness diagnosis has emerged as a new area of medical research interest. Skin diseases are among the most prevalent in the medical field, and when compared to other disease types, they are more visibly represented. At the early stages of a skin condition, it assists physicians and patients in determining the type of disease based on an image of the affected area. Thus, deep learning techniques are becoming increasingly important for the identification of skin diseases in images, and this has researchers interested. In this study, we examine the use of Hybrid deep learning technology to identify skin diseases, Hybrid deep learning framework, model performance, and model for image recognition of skin diseases. In addition, we assess the state of this field's development and forecast four potential future research areas. Our findings demonstrate that the deep learning-based approach to skin disease image recognition outperforms dermatologists' methods and other computer-aided treatment techniques in the diagnosis of skin diseases. Specifically, the multi-deep learning model fusion method exhibits the best recognition efficacy.