Detection of Breast Cancer in Mammograms through a New Features Technique

This research proposes a new framework for detection of breast cancer in mammograms. It extracts certain dynamic features to distinguish between benign and malignant mammograms. To this aim, this framework uses set of various techniques. First step we have achieved improvement on breast mammogram to improve the image accuracy based on this framework, after new method has been used for features extraction. New methods named Sparse Principal Component Analysis and Weighted Sparse Principal Component Analysis are used to select the distinctive features of the mammograms. The analyzed mammograms are then identified as benign or malignant through codebook technique is more efficient than other on the MIAS data set. The proposed framework tested on MIAS data set achieved an overall classification accuracy of 98% with codebook classifier for sequential selection of benign and malignant mammograms. Suggested method achieves good results when we have verified on various mammograms.