On optimal wavelet bases for classification of melanoma images through ensemble learning

Abstract

This article addresses the medical problem of early detection of the malignant melanoma skin cancer. We present ensemble classification of dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. The features used for classification are derived from wavelet decomposition coefficients of the image. Our research purpose is to select the best wavelet bases in terms of AUC classification performance of the ensemble. The ensemble learning is optimized by some common quality measures: accuracy, precision, F1-score, FP- rate, specificity, BER and recall. Within the statistics of our machine learning experiments the best model of melanoma uses reverse bi-orthogonal wavelet pair (3.1) and is optimized by FP-rate. This wavelet base performs very well with downscaled image resolutions which matters future small ARM-based devices for computer aided diagnosis of melanoma.