Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/20172
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dc.contributor.authorSalih, Mohammed Gasm ALseed Ali-
dc.contributor.authorSupervisor, -Ali Ahmed Alfaki Abdalla-
dc.date.accessioned2018-01-25T07:45:30Z-
dc.date.available2018-01-25T07:45:30Z-
dc.date.issued2017-11-12-
dc.identifier.citationSalih, Mohammed Gasm ALseed Ali .An Enhancement of Breast Cancer Detection Method based on an Ensemble Classification and Feature Selection Approach /Mohammed Gasm ALseed Ali Salih ;Ali Ahmed Alfaki Abdalla .-Khartoum:Sudan University of Science & Technology, College of Computer Science and Information Technology ,2017.-43p.:ill.;28cm.-M.Scen_US
dc.identifier.urihttp://repository.sustech.edu/handle/123456789/20172-
dc.descriptionThesisen_US
dc.description.abstractBreast cancer is one of the second leading causes of cancer death in women. Despite the fact that cancer is preventable and curable in primary stages, the huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer .The objective of this study is to find the smallest subset of features and using ensemblemethod that can ensure highly accurate classification of breast cancer as either benign or malignant.in this study ensemble classifier gives the maximum accuracy compared individual classification classifiersen_US
dc.description.sponsorshipSudan University of Science & Technologyen_US
dc.language.isoenen_US
dc.publisherSudan University of Science & Technologyen_US
dc.subjectEnhancement of Breasten_US
dc.subjectCancer Detection Methoden_US
dc.titleAn Enhancement of Breast Cancer Detection Method based on an Ensemble Classification and Feature Selection Approachen_US
dc.title.alternativeتحسين طريقة إكتشاف سرطان الثدي بناءً على طريقة التصنيف المتعدد وطريقة إختيار الخصائصen_US
dc.typeThesisen_US
Appears in Collections:Masters Dissertations : Computer Science and Information Technology

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