Melanoma Cancer Detection using Deep Learning

Abstract

Now a days, skin cancer is well known reason for human death. abnormal skin cells growth is known as skin cancer ,these skin cells generated on human body which exposed to the sunlight, it can generate anywhere on the human body. At early stage, most of the cancers are curable. Hence, it is required to detect skin cancer at early stage to save patient life. It is possible to recognise skin cancer at early stage with advanced technology. Here we present a novel framework using deep learning method and a local descriptor encoding strategy for recognition of dermoscopy image. In particular, the deep representations of a rescaled dermoscopy image first extricated through an exceptionally deep residual neural network, which is pre-trained on a large natural image dataset. After that, local deep descriptors are collected by order less visual statistic features depends on fisher vector encoding to build a global image representation. At last utilized the fisher vector encoded representations to arrange melanoma images utilizing a convolution neural network (CNN). This proposed system is able to generate more discriminative features to deal with large variations within melanoma classes as well as small variations among melanoma and non-melanoma classes with limited training data.