Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection
American Journal of Neural Networks and Applications
Volume 5, Issue 1, June 2019, Pages: 12-22
Received: May 10, 2019;
Accepted: Jun. 10, 2019;
Published: Jun. 29, 2019
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Nithya, Department of Mathematics, Mother Teresa Women’s University, Kodaikanal, India
Bhuvaneswari, Department of Mathematics, Mother Teresa Women’s University, Kodaikanal, India
Senthil, District Rural Development Agency, Dindigul, India
Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as lumps and microcalciﬁcations appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for womens quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this paper, we have presented a novel approach to identify the presence of breast cancer lumps in mammograms. The proposed algorithm for selecting initial cluster centers on the basis of minimal spanning tree (MST) is presented. MST initialization method for the intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images and Breast cancer patients symptoms used to predictive probability calculated by Pearson Chi-Square (χ2) test at 0.05 significance level indicate a highly significant correlation between mammography performance and clinical symptoms of breast cancer. Our findings suggest that mammography is highly efficient and promising technique.
Robust Minimal Spanning Tree Using Intuitionistic Fuzzy C-means Clustering Algorithm for Breast Cancer Detection, American Journal of Neural Networks and Applications.
Vol. 5, No. 1,
2019, pp. 12-22.
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