Extreme learning machine (ELM)-based classification of benign and malignant cells in breast cancer

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dc.authoridExtreme learning machine (ELM)-based classification of benign and malignant cells in breast canceren_US
dc.contributor.authorToprak, Abdullah
dc.date.accessioned2022-08-09T12:30:02Z
dc.date.available2022-08-09T12:30:02Z
dc.date.issued2018en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionPMID: 30222727
dc.description.abstractBackground: Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). Material/Methods: Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. Results: The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. Conclusions: It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features.en_US
dc.identifier.citationToprak, A. (2018). Extreme learning machine (ELM)-based classification of benign and malignant cells in breast cancer. Medical Science Monitor, 24, 6537-6543.en_US
dc.identifier.doi10.12659/MSM.910520
dc.identifier.endpage6543en_US
dc.identifier.issn1234-1010
dc.identifier.pmid30222727
dc.identifier.scopus2-s2.0-85053661222
dc.identifier.scopusqualityQ1
dc.identifier.startpage6537en_US
dc.identifier.urihttps://medscimonit.com/abstract/index/idArt/910520
dc.identifier.urihttps://hdl.handle.net/11468/10156
dc.identifier.volume24en_US
dc.identifier.wosWOS:000444841000003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorToprak, Abdullah
dc.language.isoenen_US
dc.publisherInternational Scientific Information, Inc.en_US
dc.relation.ispartofMedical Science Monitor
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast neoplasmsen_US
dc.subjectClassificationen_US
dc.subjectDiagnosisen_US
dc.titleExtreme learning machine (ELM)-based classification of benign and malignant cells in breast canceren_US
dc.titleExtreme learning machine (ELM)-based classification of benign and malignant cells in breast cancer
dc.typeArticleen_US

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