Ensemble Learning Approaches to Classification of Pulmonary Nodules

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dc.authoridAkan, Aydin/0000-0001-8894-5794
dc.authorwosidAkan, Aydin/P-3068-2019
dc.contributor.authorTartar, Ahmet
dc.contributor.authorAkan, Aydin
dc.date.accessioned2024-08-04T20:42:46Z
dc.date.available2024-08-04T20:42:46Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description3rd International Conference on Control, Decision and Information Technologies (CoDIT) -- APR 06-08, 2016 -- St Pauls Bay, MALTAen_US
dc.description.abstractLung cancer is one of the primary causes of cancer-related death worldwide. A computer-aided detection (CAD) can help radiologists by offering a second opinion and making the whole process faster at an early level. In this study, we propose a new classification approach for pulmonary nodule detection from CT imagery by using morphological features of nodule patterns. Ensemble learning approaches are used for classification process and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. The performance of the proposed system with random forest based on ensemble learning approaches results in an overall accuracy of 98.7 % with a sensitivity of 100 % and specificity of 97.3 % in training data set and an overall accuracy of 80.7 % with a sensitivity of 80.7 % and specificity of 80.6 % in testing dataset.en_US
dc.description.sponsorshipIEEE,Univ Malta,IEEE Syst Man & Cybernet Soc,IEEE Malta Sect,IEEE Robot & Automat Soc, Tunisia Chapter,Int Inst Innovat Ind Engn & Entrepreneurship,GDR RO,GDR MACSen_US
dc.identifier.endpage477en_US
dc.identifier.isbn978-1-5090-2188-8
dc.identifier.scopus2-s2.0-84995471485en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage472en_US
dc.identifier.urihttps://hdl.handle.net/11616/97580
dc.identifier.wosWOS:000386533900084en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2016 International Conference on Control, Decision and Information Technologies (Codit)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectpulmonary noduleen_US
dc.subjectclassificationen_US
dc.subjectcomputed tomographyen_US
dc.subjectensemble learning classifieren_US
dc.subjectbaggingen_US
dc.subjectadaboosten_US
dc.titleEnsemble Learning Approaches to Classification of Pulmonary Nodulesen_US
dc.typeConference Objecten_US

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