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Over the last decades, the incidence of skin cancer, melanoma andnon-melanoma, has increased at a continuous rate. In particular for melanoma,the deadliest type of skin cancer, early detection is important to increasepatient prognosis. Recently, deep neural networks (DNNs) have become viable todeal with skin cancer detection. In this work, we present a smartphone-basedapplication to assist on skin cancer detection. This application is based on aConvolutional Neural Network(CNN) trained on clinical images and patientsdemographics, both collected from smartphones. Also, as skin cancer datasetsare imbalanced, we present an approach, based on the mutation operator ofDifferential Evolution (DE) algorithm, to balance data. In this sense, beyondprovides a flexible tool to assist doctors on skin cancer screening phase, themethod obtains promising results with a balanced accuracy of 85% and a recallof 96%.

A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information