Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines

dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Tohka, Jussi	en
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-05-11T08:56:39Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10115
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.worldscientific.com/doi/abs/10.1142/S0129065705000013	en
dc.subject	Probabilistic neural network
dc.subject	Microscopy
dc.subject	Πιθανοτικό νευρωνικό δίκτυο
dc.subject	Μικροσκοπία
dc.title	Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines	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.keywordURI	http://skos.um.es/unesco6/230112
heal.identifier.secondary	DOI: 10.1142/S0129065705000013
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	2005
heal.bibliographicCitation	Glotsos, D., Tohka, J., Ravazoula, P., Cavouras, D. and Nikiforidis, G. (February & April 2005). Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines. International Journal of Neural Systems. 15(01n02). pp. 1-11. World Scientific Publishing: 2005.	en
heal.abstract	A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.	en
heal.publisher	World Scientific Publishing	en
heal.journalName	International Journal of Neural Systems	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false