Detection of skin cancer using Convolutional neural network

Abstract

One of the most common and fatal cancer in the universe is skin cancer which arise from skin of epidermis, the topmost layer of the skin, it can happen anywhere in the body. We can find out the cancer by early detection. Skin cancer detection is a time consuming process and very critical. So in clinical applications, the machine learning analysis of skin cancer is failed to give correct images for a model. In our paper we followed three pre-processing steps which are: a) removing the shadows from the image which is illumination correction processing, b) to find the border of the skin lesion in the segmentation part, c) feature extraction by doing the ABCD framework. Our thesis makes an attempt to implement the method of Convolutional Neural Network. Using this classification, we find out the best result in inception v3 which was trained on skin lesions and we got the accuracy of 82.4%. So, our primary focus of this thesis is to differentiate between cancerous and non-cancerous image. Then our goal is to reduce importance of one of the painful process in cancer detection which is known as biopsy. Biopsy is removing tissue from a body and later it goes to many laboratory tests.