Floating search methods in feature selection
Pattern Recognition Letters
Initial results of automated melanoma recognition
Selected papers from the 9th Scandinavian conference on Image analysis : theory and applications of image analysis II: theory and applications of image analysis II
Methodological review: Computerized analysis of pigmented skin lesions: A review
Artificial Intelligence in Medicine
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Melanoma, one of the most aggressive types of cancer, can be healed, if recognized in early stages. In order to automate the early recognition of skin cancer, a system that analyses digital epiluminescence microscopic images is used. After segmentation, 33 features representing shape and radiometric properties are calculated. In this paper the quality of the features is evaluated by applying several feature selection methods. The results show that with each selection method the feature set can be reduced to dimension four with nearly no loss of information. Results with classification rates of up to 75% are achieved and realtions between selected features and medical criteria are observed.