Skin Cancer Prediction Using Deep Learning Techniques

Skin cancer is the one of ten most common cancer types in the world. The abnormal growth of skin cells most often develops on the skin exposed to the sun. Skin cancer occurs when errors occur in the DNA of skin cells, it begins at the top of the skin. There are three types of skin cancer they are 1. Melanoma 2. Basal cell Carcinoma 3. Squamous cell skin cancer. The mutation causes the cells to grow out of control and form a mass of cancer cells. Melanoma is the most dangerous skin form of skin cancer when compared to the other types. The main symptom of skin cancer is a mole are other growth on our skin. Skin cancer symptoms include darker looking skin yellowish and eyes reddened skin, itching and excessive hair growth. There is proper technique followed to reduce the skin cancer by limiting or avoiding exposure to ultraviolet (UV) radiation. Detection of skin cancer at an earlier stage can increase the survival rate. When compared to the ancient days the rate of skin cancer peoples is increased significantly, due to modern techniques and lifestyles, direct exposure to harmful UV radiation from sun. It was estimated that in the year 2022 around 57,180 men’s and 42,600 women’s would develop Melanoma of skin.In the case of positive skin cancer, determining the type is very important because it helps to identify the most suitable treatment. In recent times the usage of image processing and mission vision in the field of health care and medical applications is increasing at a great face. In this paper we are using the convolution neural network (CNN) to detect and classified the clause of cancer based on historical data of clinical images usingRESNET.