  Over the last decades, the incidence of skin cancer, melanoma and
non-melanoma, has increased at a continuous rate. In particular for melanoma,
the deadliest type of skin cancer, early detection is important to increase
patient prognosis. Recently, deep neural networks (DNNs) have become viable to
deal with skin cancer detection. In this work, we present a smartphone-based
application to assist on skin cancer detection. This application is based on a
Convolutional Neural Network(CNN) trained on clinical images and patients
demographics, both collected from smartphones. Also, as skin cancer datasets
are imbalanced, we present an approach, based on the mutation operator of
Differential Evolution (DE) algorithm, to balance data. In this sense, beyond
provides a flexible tool to assist doctors on skin cancer screening phase, the
method obtains promising results with a balanced accuracy of 85% and a recall
of 96%.
