An effective strategy of digitized mammograms classification

摘要

In this paper, a method of Attribute Reduction Based on Discernibility Matrix and a proximal support vector machine (psvm) is integrated and used to implement a classification of digitized mammograms. The Attribute Reduction Algorithm is used to reduce useless and interfering attributes of medical images, and the proximal support vector machine that runs faster, and is easy to implement, is used to classify the medical images into two classes: normal and abnormal. It is proved by experiments that our strategy do have some good features such as simpleness, speediness and high efficiency.