title: Symbolic learning supporting early diagnosis of melanoma.
abstract: We present a classification analysis of the pigmented skin lesion images taken in white light based on the inductive learning methods by Michalski (AQ). Those methods are developed for a computer system supporting the decision making process for early diagnosis of melanoma. Symbolic (machine) learning methods used in our study are tested on two types of features extracted from pigmented lesion images: coloristic/geometric features, and wavelet-based features. Classification performance with the wavelet features, although achieved with simple rules, is very high. Symbolic learning applied to our skin lesion data seems to outperform other classical machine learning methods, and is more comprehensive both in understanding, and in application of further improvements.
authors.affiliation: Department of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Poland. grzegorz.surowka@uj.edu.pl
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Surówka
mesh_terms: D003936:Diagnosis, Computer-Assisted; D042241:Early Diagnosis; D006801:Humans; D007858:Learning; D008545:Melanoma / Q000175:diagnosis*; D012878:Skin Neoplasms / Q000175:diagnosis*