  The aim of this work is to propose an ensemble of descriptors for Melanoma
Classification, whose performance has been evaluated on validation and test
datasets of the melanoma challenge 2018. The system proposed here achieves a
strong discriminative power thanks to the combination of multiple descriptors.
The proposed system represents a very simple yet effective way of boosting the
performance of trained CNNs by composing multiple CNNs into an ensemble and
combining scores by sum rule. Several types of ensembles are considered, with
different CNN architectures along with different learning parameter sets.
Moreover CNN are used as feature extractors: an input image is processed by a
trained CNN and the response of a particular layer (usually the classification
layer, but also internal layers can be employed) is treated as a descriptor for
the image and used for training a set of Support Vector Machines (SVM).
