Performance and Analysis of Skin Cancer Detection Using Deep Learning

Skin cancer is notably one of the most dangerous expressions of this ailment, originating from DNA damage in skin cells that results in the formation of genetic abnormalities or mutations on the skin. The insidious characteristic of skin cancer lies in its inclination to gradually spread to other parts of the body, underscoring the importance of early detection for successful treatment. Given the increasing prevalence of skin cancer, its elevated mortality rate, and the significant expenses associated with medical interventions, prompt diagnosis becomes imperative. Acknowledging the severity of these challenges, researchers have developed various methods for the early identification of skin cancer. Parameters such as the symmetry, color, size, and shape of lesions are utilized to distinguish skin cancer and differentiate benign cases from melanoma. This paper explores deep learning approaches applied to the early detection of skin cancer, presenting the study's findings through tools, graphs, tables, techniques, and frameworks. This enhances comprehension and contributes to the body of knowledge in this critical field.