  Malignant melanoma is the deadliest form of skin cancer and, in recent years,
is rapidly growing in terms of the incidence worldwide rate. The most effective
approach to targeted treatment is early diagnosis. Deep learning algorithms,
specifically convolutional neural networks, represent a methodology for the
image analysis and representation. They optimize the features design task,
essential for an automatic approach on different types of images, including
medical. In this paper, we adopted pretrained deep convolutional neural
networks architectures for the image representation with purpose to predict
skin lesion melanoma. Firstly, we applied a transfer learning approach to
extract image features. Secondly, we adopted the transferred learning features
inside an ensemble classification context. Specifically, the framework trains
individual classifiers on balanced subspaces and combines the provided
predictions through statistical measures. Experimental phase on datasets of
skin lesion images is performed and results obtained show the effectiveness of
the proposed approach with respect to state-of-the-art competitors.
