Development of the probabilistic neural network–cubic least squares mapping (PNN–LSM3) classifier to assess carotid plaque’s risk

dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Θεοχαράκης, Παντελής	el
dc.contributor.author	Δημητρόπουλος, Νικόλαος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.date.accessioned	2015-04-30T08:56:09Z
dc.date.available	2015-04-30T08:56:09Z
dc.date.issued	2015-04-30
dc.identifier.uri	http://hdl.handle.net/11400/9306
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.sciencedirect.com/science/article/pii/S0167865503002277	en
dc.subject	Carotid plaque
dc.subject	Probabilistic neural network
dc.subject	Καρωτιδική πλάκα
dc.subject	Πιθανοτικό νευρωνικό δίκτυο
dc.title	Development of the probabilistic neural network–cubic least squares mapping (PNN–LSM3) classifier to assess carotid plaque’s risk	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.identifier.secondary	doi:10.1016/j.patrec.2003.10.006
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2004
heal.bibliographicCitation	Piliouras, N., Kalatzis, I., Theocharakis, P., Dimitropoulos, N. and Cavouras, D. (January 2004). Development of the probabilistic neural network - cubic least squares mapping (PNN-LSM3) classifier to assess carotid plaque's risk. Pattern Recognition Letters. 25(2). pp. 249-258. Elsevier B.V: 2004. Available from: http://www.sciencedirect.com/science/article/pii/S0167865503002277 [Accessed 14/11/2003]	en
heal.abstract	An efficient classification algorithm based on the cubic least squares mapping (LSM3) and the probabilistic neural network (PNN) classifier is proposed for assessing the carotid plaque’s risk of causing brain infarcts. Ultrasound images of 24 high-risk and 32 low-risk carotid plaques were manually segmented by an experienced physician using a custom developed software. Three textural features, related to the plaque’s internal composition, the PNN, and the PNN–LSM3 classification algorithms were used to design a classification system. PNN classification accuracy was 92.9%, misdiagnosing one high-risk and three low-risk plaques while the PNN–LSM3 managed to classify all plaques correctly. The proposed system may be of value to patient management as a second opinion tool, after it is tested on more data in a clinical environment.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Pattern Recognition Letters	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true