Abstract
Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.
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C. Selvi has no conflict of interest with Co-Author M.Suganthi. No conflict of Interest between Two Authors.
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Selvi, C., Suganthi, M. A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer. J Med Syst 42, 221 (2018). https://doi.org/10.1007/s10916-018-1082-7
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DOI: https://doi.org/10.1007/s10916-018-1082-7