  Skin cancer is a major public health problem, with over 5 million newly
diagnosed cases in the United States each year. Melanoma is the deadliest form
of skin cancer, responsible for over 9,000 deaths each year. In this paper, we
propose an ensemble of deep convolutional neural networks to classify
dermoscopy images into three classes. To achieve the highest classification
accuracy, we fuse the outputs of the softmax layers of four different neural
architectures. For aggregation, we consider the individual accuracies of the
networks weighted by the confidence values provided by their final softmax
layers. This fusion-based approach outperformed all the individual neural
networks regarding classification accuracy.
