DEEP INTO ACCURACY: EXPLORING THE PERFORMANCE OF SKIN DETECTION ALGORITHMS IN DEEP LEARNING

Skin diseases rank among the most prevalent medical conditions, given that the skin, being the outermost layer of the body, is susceptible to rapid affliction. Timely identification of these diseases is crucial to prevent them from progressing to life-threatening stages. This review explores diverse research endeavors employing deep learning technology for skin disease identification. It provides a brief overview of skin diseases, encompassing their types, datasets used for skin disease research, and techniques for data preprocessing. The review delves into the realm of deep learning, elucidating prominent methods adopted by researchers for diagnosing skin diseases. Our primary objective is to furnish a systematic literature review of skin disease detection, emphasizing the utilization of deep learning methodologies in recent research. Notably recognized for their heightened accuracy, our observations affirm that these deep learning methods in skin disease image recognition surpass the capabilities of dermatologists, various machine-based therapeutic strategies, and alternative classification methods.