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dc.contributor.authorOguz, Oguzhan
dc.contributor.authorAkbas, Cem Emre
dc.contributor.authorMallah, Maen
dc.contributor.authorTasdemir, Kasim
dc.contributor.authorGuzelcan, Ece Akhan
dc.contributor.authorMuenzenmayer, Christian
dc.contributor.authorWittenberg, Thomas
dc.contributor.authorUner, Aysegul
dc.contributor.authorCetin, A. Enis
dc.contributor.authorAtalay, Rengul Cetin
dc.date.accessioned2020-02-07T06:11:54Z
dc.date.available2020-02-07T06:11:54Z
dc.date.issued2016en_US
dc.identifier.isbn978-1-5106-0026-3
dc.identifier.issn0277-786X
dc.identifier.other1996-756X
dc.identifier.other10.1117/12.2216113
dc.identifier.urihttps://hdl.handle.net/20.500.12573/145
dc.description.abstractIn this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H&E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H&E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using HSLE stained microscopic tissue images.en_US
dc.language.isoengen_US
dc.publisherSPIE-INT SOC OPTICAL ENGINEERING, 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USAen_US
dc.relation.ispartofseriesBook Series: Proceedings of SPIE;
dc.relation.ispartofseriesVolume: 9791;
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCancer Stem Cell Detectionen_US
dc.subjectH&E Stainen_US
dc.subjectCD13 Stainen_US
dc.subjectRegion Covariance Descriptor,Region Codifference Descriptoren_US
dc.subjectOnline Learningen_US
dc.subjectEigenfaceen_US
dc.subject1-D SIFTen_US
dc.titleMixture of Learners for Cancer Stem Cell Detection Using CD13 and H&E Stained Imagesen_US
dc.typeotheren_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthor
dc.identifier.doi10.1117/12.2216113
dc.relation.publicationcategoryDiğeren_US


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