title: Different learning paradigms for the classification of melanoid skin lesions using wavelets.
abstract: We use the wavelet-based decomposition to generate the multiresolution representation of dermatoscopic images of potentially malignant pigmented lesions. Three different machine learning methods are experimentally applied, namely neural networks, support vector machines, and Attributional Calculus. The obtained results confirm that neighborhood properties of pixels in dermatoscopic images are a sensitive probe of the melanoma progression and together with the selected machine learning methods may be an important diagnostic tool.
authors.affiliation: Department of Information Technology, Jagiellonian University, Reymonta 4, 30-059 Cracow, Poland. grzegorz.surowka@uj.edu.pl
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Surowka
authors.forename: Katarzyna
authors.initials: K
authors.lastname: Grzesiak-Kopec
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*