Supervised learning of melanocytic skin lesion images

We use MLP and SVM supervised learning methods to discover patterns in the pigmented skin lesion images. This methodology can be treated as a non-invasive approach to early diagnosis of melanoma. Our feature set is composed of wavelet-based multi-resolution filters of the dermoscopy images. Feature selection is done by the Ridge linear models. Discriminating malicious from benign lesion images with the selected classifiers has sensitivity of 89.2-94.7% and specificity of 85-95%.