Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning

Abstract

Malignant melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancers in the world. Early diagnosis is crucial, since if detected early, it can be cured through a simple excision. In this paper, we present an effective approach to melanoma classification from dermoscopic images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. Shape features are then extracted from this border, while colour and texture features are obtained based on a division of the image into clinically significant regions. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and the use of our classifier ensemble to lead to statistically better recognition performance.