Ensemble of Deep Learned Features for Melanoma Classification

TL;DR

This paper presents an ensemble approach combining multiple CNN-based descriptors and classifiers to improve melanoma classification accuracy, demonstrating effectiveness on the 2018 melanoma challenge datasets.

Contribution

It introduces a simple ensemble method that boosts CNN performance by combining multiple architectures and features for melanoma classification.

Findings

Achieved strong discriminative power on melanoma datasets

Ensemble of CNNs improves classification accuracy

Using CNN responses as features enhances SVM performance

Abstract

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).