Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms

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dc.authorid, Hesheng Huang/0000-0002-4927-1667
dc.authoridHirota, Kaoru/0000-0001-5347-6182
dc.authorwosidHuang, Hesheng/JAD-1702-2023
dc.contributor.authorYan, Fei
dc.contributor.authorHuang, Hesheng
dc.contributor.authorPedrycz, Witold
dc.contributor.authorHirota, Kaoru
dc.date.accessioned2024-05-19T14:40:21Z
dc.date.available2024-05-19T14:40:21Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractBreast cancer exhibits one of the highest incidence and mortality rates among all cancers affecting women. The early detection of breast cancer reduces mortality and is crucial for prolonging life expectancy. Although mammography is the most often used screening technique in clinical practice, previous studies reviewing mammograms diagnosed by radiologists have commonly revealed false negatives and false positives. Ongoing advances in machine learning techniques have triggered new motivation for the development of computer-aided diagnosis (CAD) systems, which could be applied to assist radiologists in improving final diagnostic accuracy. In this study, an automated methodology for detecting breast cancer in mammography images is proposed based on an ensemble classifier and feature weighting algorithms. First, a novel region extraction approach is proposed to constrain the search area for suspicious breast lesions and an original pectoral removal method is proposed to avoid interference when identifying a region of interest (ROI). In addition, an effective segmentation strategy is developed to automatically identify ROIs whose textural and morphological features are then fused and weighted to generate new feature vectors using a feature weighting algorithm. Finally, an ensemble classifier model is designed using k-nearest neighbor (KNN), bagging, and eigenvalue classification (EigenClass) to determine whether a mammogram contains normal, benign, or malignant tumors based on a majority voting rule. A series of experiments was conducted using the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets, the results of which demonstrated the proposed scheme outperformed comparable algorithms.en_US
dc.description.sponsorshipJilin Provincial Department of Science and Technology, China [20210201075GX]en_US
dc.description.sponsorshipAcknowledgments This work was supported by the Jilin Provincial Department of Science and Technology, China under Grant 20210201075GX.en_US
dc.identifier.doi10.1016/j.eswa.2023.120282
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85158884427en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.eswa.2023.120282
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4948
dc.identifier.volume227en_US
dc.identifier.wosWOS:001000671600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectComputer-Aided Diagnosisen_US
dc.subjectBreast Cancer Detectionen_US
dc.subjectMammographyen_US
dc.subjectEnsemble Classifieren_US
dc.subjectFeature Weightingen_US
dc.titleAutomated breast cancer detection in mammography using ensemble classifier and feature weighting algorithmsen_US
dc.typeArticleen_US

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