Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images

dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.date.accessioned	2015-05-12T13:15:56Z
dc.date.available	2015-05-12T13:15:56Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10202
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://www.computersinbiologyandmedicine.com/article/S0010-4825(07)00158-8/abstract?cc=y=	en
dc.source	http://www.sciencedirect.com/science/article/pii/S0010482507001588	en
dc.subject	Multi-classifier systems
dc.subject	Cytological images
dc.subject	Συστήματα πολλαπλών ταξινομητών
dc.subject	Κυτταρολογικές εικόνες
dc.title	Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.contributorName	Καρδάρη, Μαρία	el
heal.contributorName	Καλατζής, Ιωάννης	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.identifier.secondary	doi:10.1016/j.compbiomed.2007.09.005
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2008
heal.bibliographicCitation	Daskalakis, A., Kostopoulos, S., Spyridonos, P., Glotsos, D., Ravazoula, P., et al. (February 2008). Design of a multi classifier system for discriminating benign from malignant thyroid nodules using routinely H&E stained cytological images. Computers in Biology and Medicine. 38(2). pp. 196-203. Elsevier B.V: 2008. Available from: http://www.sciencedirect.com/science/article/pii/S0010482507001588 [Accessed 09/11/2007]	en
heal.abstract	A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental results illustrated that the classifier combination k-NN/PNN/Bayesian and the majority vote rule enhanced significantly classification accuracy (95.7%) as compared to best single classifier (PNN: 89.6%). The proposed system was designed with purpose to be utilized in daily clinical practice as a second opinion tool to support cytopathologists’ decisions, when a definite diagnosis is difficult to be obtained.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true