A New Hybrid Method for Effective Computer Aided Diagnosis to Predict Breast Tumor in Elderly Patients
Balaji Samiraj1, Arunprasath Thiyagarajan2, Yudong Zhang3, Pallikonda Rajasekaran Murugan4, Vishnuvarthanan Govindaraj5, Vigneshwaran Senthilvel6
1Balaji Samiraj, Research Scholar, Department of ECE, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Arunprasath Thiyagarajan, Associate Professor, Department of BME, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
3Yudong Zhang, Professor, Department of Informatics Building Informatics, University of Leicester, University Road, Leicester, UK.
4Pallikonda Rajasekaran Murugan, Professor, Department of ECE, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
5Vishnuvarthanan Govindaraj, Associate Professor, Department of BME, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
6Vigneshwaran Senthilvel, Research Scholar, Department of ECE, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 29 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 31 December 2019 | PP: 312-316 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D10701284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1070.1284S219
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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 related morbidity has surged in recent years due to lack of awareness and failure to have an early prediction. With technological advancements available, doctors have put-forth their fullest effort to downsize the death-rate. Despite, certain areas of patient diagnosis, specifically with the case of breast cancers need a symbiotic improvement, and should greatly help the patients to lead a better and healthy life. For such extents, we authors like to propose a new hybrid approach that encompasses the functioning of Particle Swarm Optimization (PSO) and Improved Fuzzy C-Means (IFCM) through this paper. The technique recommended has tendered an elevation in the values of PSNR, TC and DOI, which most of the contemporary algorithms/techniques have failed to achieve, thus assisting the Doctors to have a profound idea regarding the prevalence of breast cancer and tumor.
Keywords: Improved Fuzzy C-Means (IFCM), Particle Swarm Optimization (PSO).
Scope of the Article: Computer Network