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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 effectiveapproach to targeted treatment is early diagnosis. Deep learning algorithms,specifically convolutional neural networks, represent a methodology for theimage analysis and representation. They optimize the features design task,essential for an automatic approach on different types of images, includingmedical. In this paper, we adopted pretrained deep convolutional neuralnetworks architectures for the image representation with purpose to predictskin lesion melanoma. Firstly, we applied a transfer learning approach toextract image features. Secondly, we adopted the transferred learning featuresinside an ensemble classification context. Specifically, the framework trainsindividual classifiers on balanced subspaces and combines the providedpredictions through statistical measures. Experimental phase on datasets ofskin lesion images is performed and results obtained show the effectiveness ofthe proposed approach with respect to state-of-the-art competitors.

Bucket of deep transfer learning features and classification models for melanoma detection