An ensemble classification approach for melanoma diagnosis

Malignant melanoma is the deadliest form of skin cancer, and has, among cancer types, one of the most rapidly increasing incidence rates in the world. Early diagnosis is crucial, since if detected early, its cure is simple. In this paper, we present an effective approach to melanoma identification from dermoscopic images of skin lesions based on ensemble classification. First, we perform automatic border detection to segment the lesion from the background skin. Based on the extracted border, we extract a series of colour, texture and shape features. The derived features are then employed in a pattern classification stage for which we employ a novel, dedicated ensemble learning approach to address the class imbalance in the training data and to yield improved classification performance. 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 our presented classifier ensemble to lead to statistically better recognition performance compared to other dedicated classification algorithms.