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dc.contributor.authorMacatangay, Jules Matthew A.
dc.contributor.authorRuiz Jr., Conrado R.
dc.contributor.authorUsatine, Richard P.
dc.contributor.editorSkala, Václav
dc.identifier.citationWSCG 2017: full papers proceedings: 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 55-64.en
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.description.abstractClassifying skin lesions, abnormal changes in skin, into their morphologies is the first step in diagnosing skin diseases. In dermatology, morphology is a categorization of a skin lesion’s structure and appearance. Rather than directly classifying skin diseases, this research aims to explore classifying skin lesion images into primary morphologies. For preprocessing, k-means clustering for image segmentation and illumination equalization were applied. Additionally, features utilized considered color, texture, and shape. For classification, k-Nearest Neighbors, Decision Trees, Multilayer Perceptron, and Support Vector Machines were used. To evaluate the prototype, 10-fold cross validation was applied over a dataset assembled from online resources. In experimentation, the morphologies considered were macule, nodule, papule, and plaque. Moreover, different feature subsets were tested through feature selection experiments. Experimental results on the 4-class and 3-class tests show that of the classifiers selected, Decision Trees were best, having a Cohen’s kappa of 0.503 and 0.558 respectively.en
dc.format10 s.cs
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2017: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectkožní lézecs
dc.subjectstrojové učenícs
dc.subjectpočítačové viděnícs
dc.titleA primary morphological classifier for skin lesion imagesen
dc.typekonferenční příspěvekcs
dc.subject.translatedskin lesionen
dc.subject.translatedmachine learningen
dc.subject.translatedcomputer visionen
Appears in Collections:WSCG 2017: Full Papers Proceedings

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/29545

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