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dc.contributor.authorTulum, Gökalpen_US
dc.contributor.authorDandin, Özgüren_US
dc.contributor.authorErgin, Tunceren_US
dc.contributor.authorTeomete, Uygaren_US
dc.contributor.authorCüce, Ferhaten_US
dc.contributor.authorOsman, Onuren_US
dc.date.accessioned2019-07-23T12:03:46Z
dc.date.available2019-07-23T12:03:46Z
dc.date.issued2017en_US
dc.identifier.citationTulum, G., Dandin, O., Ergin, T., Teomete, U., Cuce, F., Osman, O., & Ieee. (2017). Detection of Injured Kidney in Computed Tomography. New York: Ieee.en_US
dc.identifier.isbn9781538604403
dc.identifier.urihttps://hdl.handle.net/20.500.12294/1585
dc.descriptionOsman, Onur (Arel Author)en_US
dc.description.abstractTimely and accurate diagnosis of intraabdominal organ injuries due to trauma is critical. Computer Assisted Detection (CAD) systems are rapidly developing techniques to segment the organs or to detect the pathologies in medical applications; either automatically or semi-automatically. In this work, our aim is to propose and validate a CAD system which classifies injured kidney in Computed Tomography (CT) images. Sixteen cases containing nineteen injured and thirteen intact kidneys were considered for the validation of the method. The classification of the injured kidney was satisfactorily performed with 100% sensitivity ratio.en_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIntact Kidneyen_US
dc.subjectFeature Extractionen_US
dc.subjectClassificationen_US
dc.subjectMultilayer Perceptronen_US
dc.titleDetection of Injured Kidney in Computed Tomographyen_US
dc.title.alternativeBilgisayarlı Tomografi Görüntülerinde Hasarlı Böbrek Tespitien_US
dc.typeconferenceObjecten_US
dc.departmentMühendislik ve Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0001-7675-7999en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.coverage.doi10.1109/EBBT.2017.7956783


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