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dc.contributor.authorAkan, AYDIN
dc.contributor.authorTartar, Ahmet
dc.contributor.authorKilic, Niyazi
dc.date.accessioned2021-03-03T17:00:07Z
dc.date.available2021-03-03T17:00:07Z
dc.identifier.citationTartar A., Kilic N., Akan A., "Classification of Pulmonary Nodules by Using Hybrid Features", COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2013
dc.identifier.issn1748-670X
dc.identifier.othervv_1032021
dc.identifier.otherav_4760bfee-4855-4a5d-a1c5-2eea6e9beb9f
dc.identifier.urihttp://hdl.handle.net/20.500.12627/51547
dc.identifier.urihttps://doi.org/10.1155/2013/148363
dc.description.abstractEarly detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).
dc.language.isoeng
dc.subjectYaşam Bilimleri
dc.subjectBiyoinformatik
dc.subjectTemel Bilimler
dc.subjectTemel Tıp Bilimleri
dc.subjectBiyokimya
dc.subjectSağlık Bilimleri
dc.subjectTıp
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectBiyoloji ve Biyokimya
dc.subjectMATEMATİKSEL VE ​​BİLGİSAYAR BİYOLOJİSİ
dc.titleClassification of Pulmonary Nodules by Using Hybrid Features
dc.typeMakale
dc.relation.journalCOMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
dc.contributor.departmentİstanbul Üniversitesi , Mühendislik Fakültesi , Elektrik-Elektronik Mühendisliği Bölümü
dc.contributor.firstauthorID56115


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