Deep Learning based Skin Cancer Classifier using MobileNet

Abstract

Abstract: Skin cancer is a major problem faced by the public and it has been on the rise since the advent of global warming. The erosion of ozone has led to the Sun’s ultraviolet rays to directly reach the earth and cause many skin diseases. Primary sources of diagnosis include external examination and biopsy, histopathological analysis and dermoscopic analysis. These methods need skilled individuals or else the accuracy of diagnosis will be very low. If the skin cancer is not detected early, it may be lethal to the patient. As the cancer is external, we can use the images captured of the lesions for analysis. We propose a seven-way skin cancer classifier algorithm based on convolutional neural networks that will give results comparable to skin specialists in the diagnosis of skin cancer from skin lesion images. Transfer learning is a tool at our disposal to improve the accuracy of our system by using pre-trained models. TheHAM10000 dataset is a collection of 10000 dermoscopic images of skin lesions which can be used to train the model. We can use the MobileNet model on the dataset to build a model. We measured the accuracy of the model as categorical, top-2 and top 3 and obtained a categorical accuracy of 85%, top-2 accuracy of 91% and top-3 accuracy of 96%. This tool can assist doctors in early detection of skin cancer. Keywords: Skin Cancer Detection, Melanoma, HAM10000, Convolutional Neural Networks, Transfer Learning, MobileNet