Resolution Invariant Neural Classifiers for Dermoscopy Images of Melanoma

This article contributes to the Computer Aided Diagnosis (CAD) of melanoma pigmented skin cancer. We test back-propagated Artificial Neural Network (ANN) classifiers for discrimination in benign and malignant skin lesions. Features used for the classification are derived from wavelet decomposition coefficients of the dermoscopy image. We show the most efficient ANN setups as a function of the structure of hidden layers and the network learning algorithms. Our network topologies are limited for the sake of restrictions in memory and processing power of smartphones which are more and more popular as hand-held 'mobile' CAD devices for melanoma. We claim resolution invariance of the selected wavelet features.