Computational classification of melanocytic skin lesions

Abstract

The increasing incidence of melanoma skin cancer is alarming. The lack of objective diagnostic procedures encourages development of computer aided approaches. Presented research uses three different machine learning methods, namely the Naive Bayes classifier, the Random Forest and the K* instance-based classifier together with two metalearning algorithms: the Bootstrap Aggregating (Bagging) and the Vote Ensemble Classifier. Diagnostic accuracy of the selected methods, such as sensitivity and specificity and the area under the ROC curve, are discussed. The obtained results confirm that clinical history context and dermoscopic structures present in the images are important and can give accurate diagnostic classification of the lesions.