Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

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dc.contributor.authorAbdulla, Srwa Hasan
dc.contributor.authorSagheer, Ali Makki
dc.contributor.authorVeisi, Hadi
dc.coverage.issue2cs
dc.coverage.volume27cs
dc.date.accessioned2022-01-26T08:21:46Z
dc.date.available2022-01-26T08:21:46Z
dc.date.issued2021-12-21cs
dc.description.abstractComputer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.en
dc.formattextcs
dc.format.extent36-43cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2021 vol. 27, č. 2, s. 36-43. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2021.2.036en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/203390
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/144cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectBreast Canceren
dc.subjectMammogramen
dc.subjectPectoral Muscleen
dc.subjectK-means Clusteringen
dc.subjectSMOTEen
dc.subjectRandom Foresten
dc.titleImproving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Imagesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 27, No. 2