Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme

dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.date.accessioned	2015-05-12T13:27:02Z
dc.date.available	2015-05-12T13:27:02Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10203
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.cmpbjournal.com/article/S0169-2607(08)00039-4/abstract	en
dc.source	http://www.sciencedirect.com/science/article/pii/S0169260708000394	en
dc.subject	Astrocytomas
dc.subject	Support vector machines
dc.subject	Αστροκυτώματα
dc.subject	Μηχανές διανυσμάτων υποστήριξης
dc.title	Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Biomedical engineering
heal.classification	Τεχνολογία
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh92001188
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008009003
heal.contributorName	Αθανασιάδης, Εμμανουήλ	el
heal.contributorName	Ραβαζούλα, Παναγιώτα	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.identifier.secondary	doi:10.1016/j.cmpb.2008.01.006
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	Glotsos, D., Kalatzis, I., Spyridonos, P., Kostopoulos, S., Daskalakis, A., et al. (June 2008). Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme. Computer Methods and Programs in Biomedicine. 90(3). pp. 251-261. Elsevier B.V: 2008. Available from: http://www.cmpbjournal.com/article/S0169-2607(08)00039-4/abstract [Accessed 17/03/2008]	en
heal.abstract	Grading of astrocytomas is an important task for treatment planning; however, it suffers from significantly great inter-observer variability. Computer-assisted diagnosis systems have been propose to assist towards minimizing subjectivity, however, these systems present either moderate accuracy or utilize specialized staining protocols and grading systems that are difficult to apply in daily clinical practice. The present study proposes a robust mathematical formulation by integrating state-of-art technologies (support vector machines and least squares mapping) in a cascade classification scheme for separating low from high and grade III from grade IV astrocytic tumours. Results have indicated that low from high-grade tumours can be correctly separated with a certainty as high as 97.3%, whereas grade III from grade IV tumours with 97.8%. The overall performance was 95.2%. These high rates have been a result of applying the least squares mapping technique to features prior to classification. A significant byproduct of least squares mapping is that the number of support vectors of the SVM classifiers dropped dramatically from about 80% when no mapping was used to less than 5% when mapping was used. The latter is a clear indication that the SVM classifier has a greater potential to generalize well to new data. In this way, digital image analysis systems for automated grading of astrocytomas are brought closer to clinical practice.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true