Breast cancer is a significant public health problem in the world. According to the literature early detection improve breast cancer prognosis. Mammography is a screening tool used for early detection of breast cancer. About 10–30% cases are missed during the routine check as it is difficult for the radiologists to make accurate analysis due to large amount of data. The Microcalcifications (MCs) are considered to be important signs of breast cancer. It has been reported in literature that 30% - 50% of breast cancer detected radio graphically show MCs on mammograms. Histologic examinations report 62% to 79% of breast carcinomas reveals MCs. MC are tiny, vary in size, shape, and distribution, and MC may be closely connected to surrounding tissues. There is a major challenge using the traditional classifiers in the classification of individual potential MCs as the processing of mammograms in appropriate stage generates data sets with an unequal amount of information for both classes (i.e., MC, and Not-MC). Most of the existing state-of-the-art classification approaches are well developed by assuming the underlying training set is evenly distributed. However, they are faced with a severe bias problem when the training set is highly imbalanced in distribution. This paper addresses this issue by using classifiers which handle the imbalanced data sets. In this paper, we also compare the performance of classifiers which are used in the classification of potential MC.
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Arun K. M. N. and H. S. Sheshadri
Analysis of classifiers performance for classification of potential microcalcification
", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88783A (July 19, 2013); doi:10.1117/12.2031479; http://dx.doi.org/10.1117/12.2031479