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Improved detection of breast cancer nuclei using modular neural networks

dc.contributor.authorSchnorrenberg, F.en
dc.contributor.authorTsapatsoulis, Nicolasen
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorSchizas, Christos N.en
dc.contributor.authorKollias, S.en
dc.contributor.authorVassiliou, M.en
dc.contributor.authorAdamou, Adamos K.en
dc.contributor.authorKyriacou, Kyriacos C.en
dc.creatorSchnorrenberg, F.en
dc.creatorTsapatsoulis, Nicolasen
dc.creatorPattichis, Constantinos S.en
dc.creatorSchizas, Christos N.en
dc.creatorKollias, S.en
dc.creatorVassiliou, M.en
dc.creatorAdamou, Adamos K.en
dc.creatorKyriacou, Kyriacos C.en
dc.date.accessioned2019-11-13T10:42:15Z
dc.date.available2019-11-13T10:42:15Z
dc.date.issued2000
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54963
dc.description.abstractA modular neural network-based approach to detect and classify breast cancer nuclei stained for steroid receptors in hispathological sections is evaluated. The system named biopsy analysis support system (BASS) is designed so that it simulates closely the assessment procedures as practiced by hispathologists.en
dc.sourceIEEE Engineering in Medicine and Biology Magazineen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0033985251&doi=10.1109%2f51.816244&partnerID=40&md5=7dda5ec07a7ce0721596861fe423ec6d
dc.subjectComputer simulationen
dc.subjectarticleen
dc.subjectFemaleen
dc.subjectAlgorithmsen
dc.subjectFeedforward neural networksen
dc.subjecthumanen
dc.subjectHumansen
dc.subjectbreast canceren
dc.subjectBreast Neoplasmsen
dc.subjectalgorithmen
dc.subjecthuman tissueen
dc.subjectcancer diagnosisen
dc.subjectImmunohistochemistryen
dc.subjectReproducibility of Resultsen
dc.subjectestrogen receptoren
dc.subjecthistopathologyen
dc.subjectprogesterone receptoren
dc.subjectimmunocytochemistryen
dc.subjectOncologyen
dc.subjectImmunoenzyme Techniquesen
dc.subjectBiopsyen
dc.subjectCell Nucleusen
dc.subjectMatrix algebraen
dc.subjectPattern Recognition, Automateden
dc.subjectartificial neural networken
dc.subjectNeural Networks (Computer)en
dc.subjectImage analysisen
dc.subjectMedical imagingen
dc.subjectReceptors, Estrogenen
dc.subjectColoring Agentsen
dc.subjectReceptors, Progesteroneen
dc.subjectoptical densityen
dc.subjectImage Processing, Computer-Assisteden
dc.subjectComputer aided diagnosisen
dc.subjectROC Curveen
dc.subjectBiopsy analysis support systems (BASS)en
dc.subjectBreast cancer nucleien
dc.subjectHematoxylinen
dc.subjectModular neural networksen
dc.subjectreceptive fielden
dc.titleImproved detection of breast cancer nuclei using modular neural networksen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1109/51.816244
dc.description.volume19
dc.description.issue1
dc.description.startingpage48
dc.description.endingpage63
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :20</p>en
dc.source.abbreviationIEEE Eng.Med.Biol.Mag.en
dc.contributor.orcidSchizas, Christos N. [0000-0001-6548-4980]
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidTsapatsoulis, Nicolas [0000-0002-6739-8602]
dc.gnosis.orcid0000-0001-6548-4980
dc.gnosis.orcid0000-0003-1271-8151
dc.gnosis.orcid0000-0002-6739-8602


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