Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features

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dc.contributor.authorCalik, Nurullah
dc.contributor.authorAlbayrak, Abdulkadir
dc.contributor.authorAkhan, Asl
dc.contributor.authorTurkmen, Ilknur
dc.contributor.authorCapar, Abdulkerim
dc.contributor.authorToreyin, Behcet Ugur
dc.contributor.authorBilgin, Gokhan
dc.date.accessioned2024-04-24T17:11:23Z
dc.date.available2024-04-24T17:11:23Z
dc.date.issued2023
dc.departmentDicle Üniversitesien_US
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.en_US
dc.description.sponsorshipScientific Research Projects Coordination Department (BAP), Istanbul Technical University [ITU-BAP MAB-2020-42314]; Scientific Research Projects Coordination Department, Yildiz Technical University [2014-04-01-KAP01]en_US
dc.description.sponsorshipThis work was supported by the Scientific Research Projects Coordination Department (BAP), Istanbul Technical University, under Project ITU-BAP MAB-2020-42314, and also supported by the Scientific Research Projects Coordination Department, Yildiz Technical University, under Project 2014-04-01-KAP01.en_US
dc.identifier.doi10.1109/JBHI.2022.3218293
dc.identifier.endpage1757en_US
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.issue4en_US
dc.identifier.pmid36318553
dc.identifier.scopus2-s2.0-85141637157
dc.identifier.scopusqualityQ1
dc.identifier.startpage1747en_US
dc.identifier.urihttps://doi.org/10.1109/JBHI.2022.3218293
dc.identifier.urihttps://hdl.handle.net/11468/17458
dc.identifier.volume27en_US
dc.identifier.wosWOS:000964853800011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Journal of Biomedical and Health Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLesionsen_US
dc.subjectImage Segmentationen_US
dc.subjectHistogramsen_US
dc.subjectFeature Extractionen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectPathologyen_US
dc.subjectClassification Algorithmsen_US
dc.subjectCervical Lesionsen_US
dc.subjectCervixen_US
dc.subjectHemotoxylen And Eosinen_US
dc.subjectLocal Histogram Featuresen_US
dc.subjectCell Morphometric Featuresen_US
dc.subjectKullback-Leibler Divergenceen_US
dc.titleClassification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Featuresen_US
dc.titleClassification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features
dc.typeArticleen_US

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