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Automated Classification of Local Patches in Colon Histopathology

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dc.creator Kalkan, Habil
dc.creator Nap, Marius
dc.creator Duin, Robert P. W.
dc.creator Loog, Marco
dc.date 2012-01-01T01:00:00Z
dc.date.accessioned 2021-12-03T11:38:58Z
dc.date.available 2021-12-03T11:38:58Z
dc.identifier 9bcd6742-ab8a-4667-abd7-a5d9e473c7b5
dc.identifier https://avesis.sdu.edu.tr/publication/details/9bcd6742-ab8a-4667-abd7-a5d9e473c7b5/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/93584
dc.description An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: normal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly segmented nuclei are combined with texture features for classification. The classification is analyzed under the three scenarios: normal vs. abnormal, cancer vs. non-cancer and four-class classification on a labeled dataset consisting of 2000 patches per class which were collected from 55 different slices. The proposed method achieves 79.28% mean accuracy between normal and abnormal; 87.67% accuracy between cancer and non-cancer and 75.15% between the four classes with equal class priories.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title Automated Classification of Local Patches in Colon Histopathology
dc.type info:eu-repo/semantics/conferenceObject


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