Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images

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dc.authorid0000-0002-9087-3010en_US
dc.authorid0000-0002-6638-3855en_US
dc.authorid0000-0002-8807-7250en_US
dc.authorid0000-0002-4099-1254en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-4939-0797en_US
dc.contributor.authorAlthubiti, Sara A.
dc.contributor.authorPaul, Sanchita
dc.contributor.authorMohanty, Rajanikanta
dc.contributor.authorMohanty, Sachi Nandan
dc.contributor.authorAlenezi, Fayadh
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-10-26T12:36:33Z
dc.date.available2023-10-26T12:36:33Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractLung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms-bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)-was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.en_US
dc.identifier.citationAlthubiti, S. A., Paul, S., Mohanty, R., Mohanty, S. N., Alenezi, F., & Polat, K. (2022). Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images. Computational and Mathematical Methods in Medicine, 2022.en_US
dc.identifier.doi10.1155/2022/2733965
dc.identifier.endpage14en_US
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.pmid35693266en_US
dc.identifier.scopus2-s2.0-85131852619en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/2733965
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11795
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000811266200009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputed-Tomographyen_US
dc.subjectGray Level Cooccurrence Matrix (GLCM)en_US
dc.subject2D Convolutionen_US
dc.titleEnsemble learning framework with GLCM texture extraction for early detection of lung cancer on CT imagesen_US
dc.typeArticleen_US

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