Show simple item record

dc.contributor.authorTartar, A.
dc.contributor.authorKilic, N.
dc.contributor.authorAkan, AYDIN
dc.date.accessioned2021-03-05T08:05:12Z
dc.date.available2021-03-05T08:05:12Z
dc.identifier.citationTartar A., Kilic N., Akan A., "A New Method for Pulmonary Nodule Detection using Decision Trees", 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Osaka, Japonya, 3 - 07 Temmuz 2013, ss.7355-7359
dc.identifier.otherav_971464ea-3e5f-45a0-aa00-8b62e134f2f2
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/101693
dc.identifier.urihttps://doi.org/10.1109/embc.2013.6611257
dc.description.abstractA computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier result in 90.5 % sensitivity and 87.6 % specificity of detection performance.
dc.language.isoeng
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectSinyal İşleme
dc.subjectBiyomedikal Mühendisliği
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik ve Teknoloji
dc.subjectMÜHENDİSLİK, BİYOMEDİKSEL
dc.subjectMühendislik
dc.titleA New Method for Pulmonary Nodule Detection using Decision Trees
dc.typeBildiri
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.contributor.firstauthorID56101


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record