Breast Cancer Classification using SVM Classifier
E. Karthikeyan1, S. Venkatakrishnan2
1E.Karthikeyan, Research Scholar, Department of CIS, Annamalai University. Chidambaram.
2Dr. S.Venkatakrishnan, Assistant Professor & Dy Co-Ordinator, Engineering Wing, DDE, Annamalai University. Chidambaram.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 527-529 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7227118419/2019©BEIESP | DOI: 10.35940/ijrte.D7227.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Early detection of breast cancer is believed to enhance the chance of survival. Mammography is the best available breast imaging technique at present which uses low-dose x-rays for detecting the breast cancer early before the symptoms are experienced. The most commonly present abnormalities in mammograms that may indicate the breast malignancy are masses and microcalcifications. The prime objective of this research is to increase the diagnostic accuracy of the detection of breast cancer malignancy in Computer Aided Diagnosis (CAD) systems by developing image processing algorithms and to categorize the women into different risk groups. The evaluation of SVM classifier has been considered. Initially, tumors have been detected from mammograms with the aid of morphological processing of breast images. Then classification is done by SVM classifier using the most dominant features namely GLRLM and Difference of Gaussian (DoG) features, which have been extracted from the selected region. The algorithm has achieved an accuracy of 89.11% using SVM classifier.
Keywords: CT, SVM, GLRLM , DoG.
Scope of the Article: Classification.