Published Paper Details:

Skin cancer is defined as the proliferation and migration of aberrant epithelial tissue, the outermost layer of the skin, as a result of Mitochondrial dysfunction that results in hazardous alterations. These mutations cause skin cells to multiply quickly and become cancerous malignancies. Despite ongoing medical improvements, melanoma continues to be an issue. According to the Skin Cancer Foundation, by the age of seventy, one out of every five persons will have skin cancer. The project expects to plan a framework that will be adequately proficient to distinguish the occurrences of different sorts of skin malignancy in the body by extracting significant features from the dataset. the classification has been done by using CNN (Convolutional Neural Networks) Algorithm. For individuals, skin cancer is a terrifying affliction. The quality of early identification of skin cancer has increased as a result of the high growth rate of Melanoma skin cancer, its high treatment expenses, and death rate. Cancer cells are manually discovered, and most cases take time to treat. This article proposed an artificial skin cancer diagnostic method automatic visual and computer vision. After the morphologic images have been divided, the extraction of features approach is used to extract the properties of the injured skin cells. A deep learning-based approach called a convolutional neural network classifier is used to stratify the retrieved characteristics.