Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification

dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Πεταλάς, Π.	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.date.accessioned	2015-05-11T09:21:55Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10118
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://dl.acm.org/citation.cfm?id=1411786	en
dc.subject	Image analysis
dc.subject	Probabilistic neural networks
dc.subject	Ανάλυση εικόνας
dc.subject	Πιθανολογικά νευρωνικά δίκτυα
dc.title	Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Oncology
heal.classification	Ιατρική
heal.classification	Ογκολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85094724
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ογκολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh98002813
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2006
heal.bibliographicCitation	Spyridonos, P., Petalas, P., Glotsos, D., Cavouras, D., Ravazoula, P., et al. (June 2006). Comparative evaluation of support vector machines and probabilistic neural networks in superficial bladder cancer classification. Journal of Computational Methods in Sciences and Engineering. 6(5-6). pp. 283-292. IOS Press: 2006	en
heal.abstract	Purpose: In this paper we address the demanding diagnostic problem of classifying tumors according to the degree of their malignancy by investigating the efficiency of Support Vector Machines (SVMs) and Probabilistic neural networks (PNN). Material and methods: 129 cases of urinary bladder carcinomas were diagnosed as high or low-risk according to the WHO grading system. Each case was represented by 36 automatically extracted nuclear features. Two different classification designs based on SVMs and PNNs were tested according to their ability in differentiating superficial urinary bladder carcinomas according to the degree of malignancy. Best feature combination for each classification scheme was obtained performing an exhaustive search in feature space and employing the leave-one-out method. Results: Both classification models (SVM and PNN) resulted in a relatively high overall accuracy of 85.3% and 83.7% respectively. Descriptors of nuclear size and chromatin cluster patterns were participated in both best feature vectors that optimized classification performance of the two classifiers. Conclusion: The good performance and consistency of the SVM and PNN models render these techniques viable alternatives in the diagnostic process of assigning urinary bladder tumors grade.	en
heal.publisher	IOS Press	en
heal.journalName	Journal of Computational Methods in Sciences and Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false