Classification of cervical precursor lesions via local histogram and cell morphometric features

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dc.contributor.authorÇalık, Nurullah
dc.contributor.authorAlbayrak, Abdulkadir
dc.contributor.authorAkhan, Aslı
dc.contributor.authorTürkmen, İlknur
dc.contributor.authorÇapar, Abdülkerim
dc.contributor.authorTöreyin, Behçet Uğur
dc.contributor.authorBilgin, Gökhan
dc.contributor.authorMüezzinoğlu, Bahar
dc.contributor.authorDurak Ata, Lütfiye
dc.date.accessioned2023-05-16T13:31:10Z
dc.date.available2023-05-16T13:31:10Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Tıbbi Patoloji Ana Bilim Dalı
dc.description.abstractCervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.
dc.description.sponsorshipScientific Research Projects Coordination Department (BAP), Istanbul Technical University ; Yildiz Technical Universityen_US
dc.identifier.citationÇalık, N., Albayrak, A., Akhan, A., Türkmen, İ., Çapar, A., Töreyin, B. U. ... Durak Ata, L. (2023). Classification of cervical precursor lesions via local histogram and cell morphometric features. IEEE Journal of Biomedical and Health Informatics, 27(4), 1747-1757. https://doi.org/10.1109/JBHI.2022.3218293
dc.identifier.doi10.1109/JBHI.2022.3218293
dc.identifier.endpage1757
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.issue4
dc.identifier.pmid36318553
dc.identifier.scopus2-s2.0-85141637157
dc.identifier.scopusqualityQ1
dc.identifier.startpage1747
dc.identifier.urihttps://doi.org/10.1109/JBHI.2022.3218293
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10947
dc.identifier.volume27
dc.identifier.wos000964853800011en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorMüezzinoğlu, Bahar
dc.language.isoen
dc.publisherIEEE-Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Journal of Biomedical and Health Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectCell Morphometric Features
dc.subjectCervical Lesions
dc.subjectCervix
dc.subjectHemotoxylen and Eosin
dc.subjectKullback-Leibler Divergence
dc.subjectLocal Histogram Features
dc.titleClassification of cervical precursor lesions via local histogram and cell morphometric features
dc.typeArticle

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