Genetic Association Rule Mining Using Intensity Histogram and GLCM features

Abstract Breast cancer is the leading cause of cancer death among women. Screening mammography is the only method currently available for the reliable detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% if women age 50 and older have regular mammograms. The detection rate can be increased 5-15% by providing the radiologist with results from a computer-aided diagnosis (CAD) system acting as a second opinion. However, among screening mammograms routinely interpreted by radiologists, very few (approximately 0.5%) cases actually have breast cancer. It would be beneficial if an accurate CAD system existed to identify normal mammograms and thus allowing the radiologist to focus on suspicious cases. This strategy could reduce the radiologist's workload and improve screening performance. Image mining is concerned with knowledge discovery in image databases. Since mammography is considered as the most effective means for breast cancer diagnosis, this paper introduces multi dimensional genetic association rule mining for classification of mammograms. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining. The purpose of our experiments is to explore the feasibility of data mining approach.. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist medical staff and improve the accuracy of mammogram detection. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.