Multiclassification for Breast Cancer Image Using Voting Techniques

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dc.contributor.author Hassan Abdalla, Ahmed Ali
dc.contributor.author Mohamed Alhag, Alobed
dc.date.accessioned 2018-11-25T15:12:23Z
dc.date.available 2018-11-25T15:12:23Z
dc.date.issued 2018-09
dc.identifier.issn 2984-8628
dc.identifier.uri http://hdl.handle.net/123456789/491
dc.description Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage, it is the primary imaging modality for diagnosis of breast cancer, the basic idea of this paper is to participate in the efforts of enhancing the accuracy in medical image classification. Wepresented a classification method based on multi-classifier voting method that can aid the physician in a mammogram image classification. The study emphasizes five phases starting with the collection of images, pre-processing (image cropping of ROI), features extracting, classification and Development of multi-classifier followed by testing and evaluation. The experimental results show that the voting achieves an accuracy of 90.04%which is a good classification result compared to individual ones. en_US
dc.description.abstract Breast cancer is the most common malignancy disease that affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage, it is the primary imaging modality for diagnosis of breast cancer, the basic idea of this paper is to participate in the efforts of enhancing the accuracy in medical image classification. Wepresented a classification method based on multi-classifier voting method that can aid the physician in a mammogram image classification. The study emphasizes five phases starting with the collection of images, pre-processing (image cropping of ROI), features extracting, classification and Development of multi-classifier followed by testing and evaluation. The experimental results show that the voting achieves an accuracy of 90.04%which is a good classification result compared to individual ones. en_US
dc.description.sponsorship Shendi University en_US
dc.language.iso en_US en_US
dc.publisher Donnish Journal of Mathematics and Computer Science Research en_US
dc.relation.ispartofseries Vol. 4(1) pp.;001-005 November, 2018
dc.subject Mammograms en_US
dc.subject Breast cancer en_US
dc.subject Cancer en_US
dc.subject Multi-classifier voting en_US
dc.subject Early detection en_US
dc.subject Image classification en_US
dc.title Multiclassification for Breast Cancer Image Using Voting Techniques en_US
dc.type Article en_US


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