BREAST CANCER DETECTION BY RULE-BASED CLASSIFICATION USING HARMONY SEARCH FROM MAMMOGRAM
Abstract
-- Breast tumour converted cancer is the leading cause of cancer death among women. Screening mammography is the reliable and low cost method currently available for detection of early and potentially curable breast cancer. Research indicates that the mortality rate could decrease by 30% to 40% if women age 50 and older have regular mammograms. The detection rate can be increased 10-20% 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. Masses, Calcification and Architectural distortion are the three major signs notable as causes for cancer recognized using mammogram images
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 approach involves Image Pre-processing resources to extract morphological features from tumours in mammograms and Image Mining to classify them as benign or malignant.
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. Images from MIAS repository were used for the experiments. The results showed the efficacy of the proposed method and system, which reduced the false positive and false negative rates, and allowed a more efficient decision-making process.
