Abstract
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.
Highlights
Breast cancer is characterized by uncontrolled growth of epithelial cells with an acquired ability of local invasion and distant metastatic dissemination
The biggest problem in medical science includes the diagnosis of disease since the reason of breast cancer is unknown, scientists know some of the risk factors like ageing, genetic risk factors, family history, menstrual periods, not having children, obesity, alcohol, overweight, etc. [1,2, 4]
The proposed approach utilizes initialization method based on minimal spanning tree (MST) is proposed to compute initial cluster centers for the Intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images
Summary
Breast cancer is characterized by uncontrolled growth of epithelial cells with an acquired ability of local invasion and distant metastatic dissemination. Saheb Basha and Satya Prasad, suggested novel approach to automatically detect the breast cancer mass in mammograms using morphological operators and fuzzy c – means clustering algorithm [4]. High mortality rate of breast cancer in Pakistan is due to the poverty, lack of awareness about cancer and its detection methods and high cost as well as fear of mammography testing and other diagnostic procedures [8,9]. Objective of current study was to provide an insight in better diagnosis of breast cancer through statistical evaluation of sensitivity, specificity, predictive accuracy and probability of mammography based breast tumor detection. The proposed approach utilizes initialization method based on MST is proposed to compute initial cluster centers for the Intuitionistic fuzzy c-means clustering algorithm for clear to identify of abnormalities for mammography images. In addition to all these performance evaluation measures, predictive probability of mammography screening was evaluated through Pearson chi square analysis
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