Mitosis detection using generic features and an ensemble of cascade adaboosts

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dc.authorid0000-0002-8649-6013
dc.authorid0000-0002-8649-6013en_US
dc.contributor.authorTek, Faik Borayen_US
dc.date.accessioned2023-02-13T12:25:16Z
dc.date.available2023-02-13T12:25:16Z
dc.date.issued2013-05-30
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractContext: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. Aims: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object -level descriptions and thus require minimal segmentation. Materials and Methods: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. Statistical Analysis Used: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F -measure, and region overlap ratio measures. Results: We tested our features with two different classifier configurations: 1)An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non -cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F -measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape?based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. Conclusions: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationTek, F. B. (2013). Mitosis detection using generic features and an ensemble of cascade adaboosts. Journal of Pathology Informatics, 4(1), 1-6. doi:10.4103/2153-3539.112697en_US
dc.identifier.endpage6
dc.identifier.issue1
dc.identifier.pmidPMID: 23858387
dc.identifier.pmid23858387en_US
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/5368
dc.identifier.urihttp://dx.doi.org/10.4103/2153-3539.112697
dc.identifier.volume4
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTek, Faik Borayen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Pathology Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMitosis detectionen_US
dc.subjectArea granulometryen_US
dc.subjectCascade adaboosten_US
dc.subjectCost‑sensitive learningen_US
dc.subjectEnsemble classifieren_US
dc.titleMitosis detection using generic features and an ensemble of cascade adaboostsen_US
dc.typeArticleen_US

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