Detection of Melanoma Skin Cancer with Deep Neural Networks

Abstract

Detection of skin cancer involves several steps of examinations first being visual diagnosis that is followed by dermoscopic analysis, a biopsy, and histopathological examination. The classification of skin lesions in the first step is critical and challenging as classes vary by minute appearance in skin lesions. Deep convolutional neural networks (CNNs) have great potential in multicategory image-based classification by considering coarse-to-fine image features. This study aims to demonstrate how to classify skin lesions, in particular, melanoma, using CNN trained on data sets with disease labels. We developed and trained our own CNN model using a subset of the images from International Skin Imaging Collaboration (ISIC) Dermoscopic Archive. To test the performance of the proposed model, we used a different subset of images from the same archive as the test set. Our model is trained to classify images into two categories: malignant melanoma and nevus and is shown to achieve excellent classification results with high test accuracy (91.16%) and high performance as measured by various metrics. Our study demonstrated the potential of using deep neural networks to assist early detection of melanoma and thereby improve the patient survival rate from this aggressive skin cancer.