Urinary bladder tumor grade diagnosis using on-line trained neural networks

dc.contributor.author	Τασουλής, Δημήτρης Κ.	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Παυλίδης, Νίκος Γ.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.date.accessioned	2015-05-08T11:08:17Z
dc.date.available	2015-05-08T11:08:17Z
dc.date.issued	2015-05-08
dc.identifier.uri	http://hdl.handle.net/11400/9959
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://link.springer.com/chapter/10.1007/978-3-540-45224-9_29	en
dc.subject	Algorithm
dc.subject	Classification
dc.subject	Αλγόριθμος
dc.subject	Ταξινόμηση
dc.title	Urinary bladder tumor grade diagnosis using on-line trained neural networks	en
heal.type	conferenceItem
heal.generalDescription	Proceedings, Part I.	en
heal.classification	Medicine
heal.classification	Neural computers
heal.classification	Ιατρική
heal.classification	Νευρωνικό δίκτυο
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh87008041
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Νευρωνικό δίκτυο
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.contributorName	Βραχάτης, Μιχαήλ Ν.	el
heal.identifier.secondary	DOI 10.1007/978-3-540-45224-9_29
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2003
heal.bibliographicCitation	Tasoulis, D., Spyridonos, P., Pavlidis, N., Cavouras, D., Ravazoula, P., et al. (2003). Urinary bladder tumor grade diagnosis using on-line trained neural networks. Proceedings of the 7th International Conference KES 2003. Oxford, UK, September 2003.	en
heal.abstract	This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, for the assignment of tumors grade. One hundred twenty nine cases were classified according to the WHO grading system by experienced pathologists in three classes: Grade I, Grade II and Grade III. 36 morphological and textural, cell nuclei features represented each case. These features were used as an input to the ANN classifier, which was trained using a novel stochastic training algorithm, namely, the Adaptive Stochastic On-Line method. The resulting automated classification system achieved classification accuracy of 90%, 94.9% and 97.3% for tumors of Grade I, II and III respectively.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference kes 2003	en
heal.conferenceItemType	full paper