Resolution invariant wavelet features of melanoma studied by SVM classifiers

doi:10.1371/journal.pone.0211318

Resolution invariant wavelet features of melanoma studied by SVM classifiers

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Resolution invariant wavelet features of melanoma studied by SVM classifiers

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dc.contributor.author	Surówka, Grzegorz [SAP11016006]	pl
dc.contributor.author	Ogorzałek, Maciej [SAP11018332]	pl
dc.date.accessioned	2019-03-06T14:47:19Z
dc.date.available	2019-03-06T14:47:19Z
dc.date.issued	2019	pl
dc.identifier.uri	https://ruj.uj.edu.pl/xmlui/handle/item/69907
dc.language	eng	pl
dc.rights	Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/pl/legalcode	*
dc.title	Resolution invariant wavelet features of melanoma studied by SVM classifiers	pl
dc.type	JournalArticle	pl
dc.abstract.en	This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks.	pl
dc.description.volume	14	pl
dc.description.number	2	pl
dc.identifier.doi	10.1371/journal.pone.0211318	pl
dc.identifier.eissn	1932-6203	pl
dc.title.journal	PLoS ONE	pl
dc.language.container	eng	pl
dc.affiliation	Wydział Fizyki, Astronomii i Informatyki Stosowanej : Zakład Technologii Informatycznych	pl
dc.subtype	Article	pl
dc.identifier.articleid	e0211318	pl
dc.rights.original	CC-BY; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0	pl
dc.identifier.project	ROD UJ / OP	pl
.pointsMNiSW	[2019 A]: 100