Skin Cancer Prediction Comparative Analysis using TL and NNs

Abstract

The skin is the body’s outermost layer, concealing/covering numerous biological organs, muscles, and other innumerable body parts. The study found that the body’s exposure to ultraviolet radiation is the main contributor to skin cancer (UV). There are several layers to the skin, but the epidermis and dermis are where cancer first appears. Changes in your skin or the appearance of a mole in many locations on your body are the most common warning signs. The only way to prevent cancer is to stay as far away from UV rays as you can, which would stop your skin from coming into contact with the disease. According to statistics, cases of this cancer have not only increased but are increasing swiftly as a result of the ozone layer’s deterioration, which causes it to stop emitting dangerous light and, as a result, come into contact with our skin. For the following issue, numerous different strategies include machine learning, DL, and TL are being used. Naive Bayes, logistic regression, random forest, decision tree, artificial NN, and convolutional NN are just a few of the numerous techniques used. The study makes an effort to put both TL and DL techniques to use in order to provide a result that shows which performs better for the next challenge.