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dc.contributor.authorKaya, Aydin
dc.contributor.authorKeceli, Ali Seydi
dc.contributor.authorCan, Ahmet Burak
dc.date.accessioned2021-06-07T07:30:05Z
dc.date.available2021-06-07T07:30:05Z
dc.date.issued2019
dc.identifier.issn1300-1884
dc.identifier.urihttp://dx.doi.org/10.17341/gazimmfd.416530
dc.identifier.urihttp://hdl.handle.net/11655/24597
dc.description.abstractNodule characteristics used in the evaluation of lung nodules are generally subjective assessments of the expert opinions. Among these characteristics, the most known and used one for prediction is the degree of malignancy. In the classification studies in the literature, deep features are used besides the traditional features extracted from the nodule appearance and morphological structure. In this study, traditional features, deep features, and the combinations both used in predicting the nodule characteristics. Four classification algorithms with different structures are evaluated for predicting nodule characteristics. Reference data sets of nodule characteristics were generated by means of majority voting from subjective assessments of radiologists. These generated data sets generally have large unbalanced class distributions. Data balancing procedure has been applied to examine the effect of this condition on classification results. With the combinations of these methods, effects of different classification models on the classification accuracy, sensitivity and specificity are examined. The results of the experiments shown that the classification strategy needs to be specifically determined starting from the used features to the classification algorithm according to the performance criterion to be achieved.
dc.language.isotur
dc.relation.isversionof10.17341/gazimmfd.416530
dc.rightsAttribution 4.0 United States
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdeep features
dc.subjectimage processing
dc.subjectNodule characteristics
dc.subjectpulmonary nodules
dc.subjecttransfer learning
dc.titleExamination Of Various Classification Strategies In Classification Of Lung Nodule Characteristics
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalJournal Of The Faculty Of Engineering And Architecture Of Gazi University
dc.contributor.departmentBilgisayar Mühendisliği
dc.identifier.volume34
dc.identifier.issue2
dc.description.indexWoS


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Attribution 4.0 United States
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