Skin Cancer Detection and Control Techniques Using Hybrid Deep Learning Techniques

More than 70% of skin cancer fatalities are caused by the deadliest type of the disease, melanoma. Malignant melanocytes give rise to melanomas. Melanoma would have a better ranking based on years lost to cancer because relatively young people are affected by this cancer. The average age at which melanoma is discovered in patients is relatively young, and the overall number of melanoma sufferers is rising. As a result, it is hypothesized that Caucasian populations are experiencing an increase in the overall burden of melanoma. There is a large potential benefit for the prevention of melanoma because the overall burden of the disease is rising, prognosis strongly depends on the time of diagnosis, as well as the majority crucially, effective treatments for advanced stages are missing. However, the majority of the known risk factors for melanoma, including pale skin tone, freckles, light eyes, advanced age, history of sunburns, clinical atypical nevi, past melanoma, and family history of the disease, are not treatable. The only possible outcomes are sun exposure and sunburns. Melanoma preventive strategies include sun protection techniques. Comprehensive sun protection programs have been in place for more than ten years in several high-risk nations, and public awareness of the benefits of sun protection is strong. The general public's sun exposure habits have not changed even though these public health efforts have raised awareness of skin cancer and the harmful effects of excessive sun exposure. Several researchers have expressed an interest in melanoma early detection, and a tremendous amount of work has been done on melanoma diagnosis. Several approaches to early detection were explored in this research work, along with the benefits and drawbacks of each approach.