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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578192
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dc.contributor.authorDheeb Albashish
dc.contributor.authorShahnorbanun Sahran (UKM)
dc.contributor.authorAzizi Abdullah (UKM)
dc.contributor.authorNordashima Abd Shukor (UKM)
dc.contributor.authorSuria Hayati Md Pauzi (UKM)
dc.date.accessioned2023-11-06T02:59:04Z-
dc.date.available2023-11-06T02:59:04Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:113375
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578192-
dc.descriptionThe Gleason grading system assists in evaluating the prognosis of men with prostate cancer. Cancers with a higher score are more aggressive and have a worse prognosis. The pathologists observe the tissue components (e.g. lumen, nuclei) of the histopathological image to grade it. The differentiation between Grade 3 and Grade 4 is the most challenging, and receives the most consideration from scholars. However, since the grading is subjective and time-consuming, a reliable computer-aided prostate cancer diagnosing techniques are in high demand. This study proposed an ensemble computer-added system (CAD) consisting of two single classifiers: a) a specialist, trained specifically for texture features of the lumen and the other for nuclei tissue component; b) a fusion method to aggregate the decision of the single classifiers. Experimental results show promising results that the proposed ensemble system (area under the ROC curve (Az) of 88.9% for Grade 3 versus Grad 4 classification task) impressively outperforms the single classifier of nuclei (Az=87.7) and lumen (Az=86.6).
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-6
dc.rightsUKM
dc.subjectEnsemble machine learning
dc.subjectGleason grading system
dc.subjectLumen
dc.subjectNuclei
dc.subjectProstate cancer histological image
dc.subjectTissue components
dc.titleLumen-nuclei ensemble machine learning system for diagnosing prostate cancer in histopathology images
dc.typeJournal Article
dc.format.volume25
dc.format.pages39-48
dc.format.issueSpecial Issue
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