On optimal wavelet bases for classification of skin lesion images through ensemble learning

In order to recognize early symptoms of melanoma, the fatal cancer of the skin, systems for computer aided melanoma diagnosis have been developed for years. In this work we analyze an ensemble-based binary classifier for discriminating melanoma from dysplastic nevus utilizing wavelet-based features of the dermatoscopic skin lesion images. The multiresolution decomposition of the dermatoscopy images is done through wavelet packets. We search for the optimal wavelet base maximizing the quality of the classifier in terms of AUC (Area Under Curve) for models optimized by some common quality measures: accuracy, precision, Fl-score, FP-rate, specificity, BER and recall. Within the statistics of our experiments reverse bi-orthogonal wavelet rbio 3.1 makes the best wavelet model of melanoma.