Comparative Analysis of Skin cancer Prediction Using Neural Networks and Transfer Learning

The skin is the external component of the body that hides/covers various biological organs, muscles, and countless body components. According to the study, the leading cause of skin cancer in the body is the body's exposure to ultraviolet radiation (UV). The skin has multiple layers, but cancer starts in two of them: the epidermis and the dermis. The most prevalent indicators are changes in your skin or the emergence of a mole in multiple spots on your skin. The sole strategy to avoid cancer is to avoid UV rays as much as possible, which would prevent your skin from getting into touch with the cancer. Statistics suggest that instances of this cancer have not only risen, but are growing quickly, as the ozone layer degrades, leading it to no longer emit hazardous light and, as a consequence, our skin comes into touch with it. Many various approaches like machine learning, deep learning and transfer learning are being employed for the following issue. The many approaches employed include naive bayes, logistic regression, random forest, decision tree, artificial neural network and convolutional neural network. The study attempts to apply the deep learning techniques and transfer learning methods to offer a result to indicate which performs better for the following task.