Breast cancer has become a common health problem in developed and developing countries during the last decades and also the leading cause of mortality in women each year. Mammogram is a special x-ray examination of the breast made with specific x-ray equipment that can often find tumors too small to be felt. In this paper, the classification of microcalcification in digital mammogram is achieved by using Stochastic Neighbor Embedding (SNE) for reducing high dimensionality data into relatively low dimensional data and K-Nearest Neighbor (KNN) Classifier. This system classifies the mammogram images into normal or abnormal, and the abnormal severity into benign or malignant. Mammography Image Analysis society (MIAS) database is used to evaluate the proposed system. The experiments demonstrate that the proposed method can provide better classification rate.

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Stochastic Neighbor Embedding K-nearest Neighbor Digital Mammograms Microcalcifications