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

Ensemble of Deep Learned Features for Melanoma Classification