NCA-based hybrid convolutional neural network model for classification of cervical cancer on gauss-enhanced pap-smear images

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dc.authorid0000-0001-5071-4616en_US
dc.contributor.authorBingöl, Harun
dc.date.accessioned2022-07-22T08:03:03Z
dc.date.available2022-07-22T08:03:03Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.descriptionReceived: 25 February 2022, Revised: 19 April 2022, Accepted: 29 April 2022.en_US
dc.description.abstractCervical cancer is a very serious disease that deeply affects women's lives, often resulting in death. This type of cancer, which is very common in women, is diagnosed at an early stage and is of vital importance for the success of the treatment. Pap-smear tests are used by physicians as the primary diagnostic tool to diagnose the disease. In this study, a hybrid deep model is proposed to classify pap-smear images to detect cervical cancer. In addition, the Gaussian method was applied to improve the images in the original dataset. Feature maps were taken from both the original dataset and the Gaussian-enhanced dataset in the built hybrid architecture, which used Darknet53 and Mobilenetv2 models as the base. After these obtained feature maps were combined, useless features were extracted and the number of features was reduced by using the Neighborhood Component Analysis (NCA) dimension reduction method. Finally, this optimized feature map was classified into different classifiers. As a result of the experimental studies, it was determined that the proposed hybrid model performed better when compared to other studies in the literature and the accuracy rate was 98.90% in the Support Vector Machines (SVM) classifier.en_US
dc.identifier.citationBingol, H. (2022). NCA‐based hybrid convolutional neural network model for classification of cervical cancer on gauss‐enhanced pap‐smear images. International Journal of Imaging Systems and Technology.en_US
dc.identifier.doi10.1002/ima.22751
dc.identifier.endpage12en_US
dc.identifier.issn0899-9457en_US
dc.identifier.issn1098-1098en_US
dc.identifier.scopus2-s2.0-85130247162en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1002/ima.22751
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1171
dc.identifier.wosWOS:000797011900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBingöl, Harun
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofInternational Journal of Imaging Systems and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCervical canceren_US
dc.subjectDeep learningen_US
dc.subjectGauss methoden_US
dc.subjectNCAen_US
dc.subjectPap-smear imagesen_US
dc.titleNCA-based hybrid convolutional neural network model for classification of cervical cancer on gauss-enhanced pap-smear imagesen_US
dc.typeArticleen_US

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