Classification of digital mammography algorithm based on rough set theory

Breast cancer represents the second leading cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents an efficient classification algorithm in digital mammograms in the context of rough set theory. Feature extractions acquired in this work are derived from the gray-level co-occurrence matrix. The features are extracted, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the classification are built and the quadratic distance function used to determines similarity between a query and database image. The experimental results show that the proposed algorithm performs well reaching over 85% in accuracy.