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.
Part of the book: Breast Cancer and Breast Reconstruction
The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.
Part of the book: Pattern Recognition