Intelligent Classification Technique for Breast Cancer
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 July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2313-2316 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8611088619/2019©BEIESP | DOI: 10.35940/ijeat.F8611.088619
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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: Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.
Keywords: CT, SVM, ANN, GLCM, Fuzzy Cluster.