A Review on Transfer Learning Approaches for Skin Melanoma Classification

Abstract

Skin is important organ of our body which covers muscles, bones, and other parts of body. Melanoma is a kind of skin cancer that begins in melanocytes cell. It can influence on the skin only, or it may expand to the bones and organs. It is less common, but more serious and aggressive than other types of skin cancer. Majority of deaths related to skin cancer occur due to Melanoma over the world. For effective treatment it is very important to melanoma identified earlier as possible. As well as detection of the stages of melanoma to recognize depth of spreading of melanocyte cell in other organ of body. Process of Detection of Skin cancer is difficult, expensive, and time-consuming process. Purpose of this research review is to more accurate recognition the types of Melanomas and decrease ratio of false diagnosis using transfer learning model for melanoma classification using AlexNet, VggNet and ResNet. The working of the different transfer learning model, its pros. and cons. Are discuss in this paper.