Artificial neural network-based classification system for lung nodules on computed tomography scans

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dc.authorid0000-0002-4641-2866en_US
dc.authorid0000-0001-6559-1399en_US
dc.authorid0000-0002-3303-8318en_US
dc.contributor.authorDandil, Emre
dc.contributor.authorÇakıroğlu, Murat
dc.contributor.authorEksi, Ziya
dc.contributor.authorÖzkan, Murat
dc.contributor.authorKurt, Özlem Kar
dc.contributor.authorCanan, Arzu
dc.date.accessioned2021-06-23T19:37:02Z
dc.date.available2021-06-23T19:37:02Z
dc.date.issued2014
dc.departmentBAİBÜ, Bolu Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description6th International Conference on Soft Computing and Pattern Recognition (SoCPaR) -- AUG 11-14, 2014 -- Tunis, TUNISIAen_US
dc.description.abstractLung cancer is the most common type of cancer among various cancers with the highest mortality rate. The fact that nodules that form on the lungs are in different shapes such as round or spiral in some cases makes their detection difficult. Early diagnosis facilitates identification of treatment phases and increases success rates in treatment. In this study, a holistic Computer Aided Diagnosis (CAD) system has been developed by using Computed-Tomography (CT) images to ensure early diagnosis of lung cancer and differentiation between benign and malignant tumors. The designed CAD system provides segmentation of nodules on the lobes with neural networks model of Self-Organizing Maps (SOM) and ensures classification between benign and malignant nodules with the help of ANN (Artificial Neural Network). Performance values of 90.63% accuracy, 92.30% sensitivity and 89.47% specificity were acquired in the CAD system which utilized a total of 128 CT images obtained from 47 patients.en_US
dc.description.sponsorshipMIR Labs, IEEE, Regim Lab, IEEE Syst Man & Cybernet Soc, Tunisia Chapter, IEEE Tunisia Sect, IEEE Computat Intellignece Soc, Sustainable Innovat Tunisia, IEEE Sfax Subsect, Tunisair Offi Carrieren_US
dc.identifier.endpage386en_US
dc.identifier.isbn978-1-4799-5934-1
dc.identifier.scopus2-s2.0-84922785732en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage382en_US
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7008037
dc.identifier.urihttps://hdl.handle.net/20.500.12491/8095
dc.identifier.wosWOS:000380429900066en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖzkan, Murat
dc.institutionauthorKurt, Özlem Kar
dc.institutionauthorCanan, Arzu
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2014 6Th International Conference Of Soft Computing And Pattern Recognition (Socpar)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectlung canceren_US
dc.subjectlung noduleen_US
dc.subjectCADen_US
dc.subjectCT imagesen_US
dc.subjectANN classificationen_US
dc.titleArtificial neural network-based classification system for lung nodules on computed tomography scansen_US
dc.typeConference Objecten_US

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