Ensemble of Deep Learned Features for Melanoma Classification
This work addresses melanoma detection, a critical medical imaging problem, but it is incremental as it builds on existing ensemble and feature extraction methods.
The paper tackles melanoma classification by proposing an ensemble of deep learned features, achieving strong discriminative power on the melanoma challenge 2018 datasets through a combination of multiple CNN descriptors and SVM training.
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).