Detection of Melanoma from Skin Lesion Images using Deep Learning Techniques

Abstract

Cancer develops when cells in any part of the body start to grow out of control. It can spread to other parts of the body. Melanoma is a type of skin cancer that is developed when melanocytes i.e. cells which produce melanin (the pigment which is responsible for the perceived color of skin) begin to grow out of control. Melanoma is dangerous as it has a high tendency to spread to other parts of the body, if not detected early and left untreated. In this paper, we use deep learning techniques to build a classification system to categorise a skin lesion into malignant and benign. This system relies on a dataset which consists of skin lesion images from various sites on the body. We augment the dataset using appropriate transformations and evaluate the classification system using various metrics. The different models used in this implementation are compared based on the metrics to find the superior performing model. ResNet-50 as per the results of sensitivity, specificity and accuracy has the best results among the other three with values 99.7%, 55.67%, 93.96% respectively.