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dc.contributor.authorAkan, A.
dc.contributor.authorTartar, A.
dc.contributor.authorKilic, N.
dc.date.accessioned2021-03-03T11:35:05Z
dc.date.available2021-03-03T11:35:05Z
dc.identifier.citationTartar A., Akan A., Kilic N., "A Novel Approach to Malignant-Benign Classification of Pulmonary Nodules by Using Ensemble Learning Classifiers", 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Illinois, Amerika Birleşik Devletleri, 26 - 30 Ağustos 2014, ss.4651-4654
dc.identifier.othervv_1032021
dc.identifier.otherav_28c16a66-a9fd-4a0e-aa40-3eef81c66f44
dc.identifier.urihttp://hdl.handle.net/20.500.12627/32245
dc.identifier.urihttps://doi.org/10.1109/embc.2014.6944661
dc.description.abstractComputer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.
dc.language.isoeng
dc.subjectBiyomedikal Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectSinyal İşleme
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectMÜHENDİSLİK, BİYOMEDİKSEL
dc.titleA Novel Approach to Malignant-Benign Classification of Pulmonary Nodules by Using Ensemble Learning Classifiers
dc.typeBildiri
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.contributor.firstauthorID143249


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