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dc.contributor.authorGünaydın, Özge
dc.contributor.authorGünay, Melike
dc.contributor.authorŞengel, Öznur
dc.date.accessioned2020-02-20T07:24:12Z
dc.date.available2020-02-20T07:24:12Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.urihttps://hdl.handle.net/11413/6248
dc.description.abstractLung cancer is a kind of difficult to diagnose and dangerous cancer. It commonly causes death both men and women so fast accurate analysis of nodules is more important for treatment. Various methods have been used for detecting cancer in early stages. In this paper, machine learning methods compared while detect lung cancer nodule. We applied Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Decision Trees and Artificial Neural Networks machine learning methods to detect anomaly. We compared all methods both after preprocessing and without preprocessing. The experimental results show that Artificial Neural Networks gives the best result with 82,43% accuracy after image processing and Decision Tree gives the best result with 93,24% accuracy without image processing.
dc.language.isoen_UStr_TR
dc.publisherInternational Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT)tr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectLung Cancer
dc.subjectClassification
dc.subjectMachine Learning
dc.subjectArtificial Neural Networks
dc.subjectSupport Vector Machines
dc.subjectDecision Trees
dc.subjectNaive Bayes
dc.subjectAkciğer Kanseri
dc.subjectSınıflandırma
dc.subjectMakine Öğrenme
dc.subjectYapay Sinir Ağları
dc.subjectVektör Makineleri Desteklemek
dc.subjectKarar Ağaçları
dc.titleComparison of lung cancer detection algorithms
dc.typeconferenceObjecttr_TR
dc.relation.journal2019 Scientıfıc Meeting on Electrical-Electronics & Biomedical Enginering and Computer Science (EBBT)tr_TR


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States