D.K.T.E. Society’s Textile And Engineering Institute, Ichalkaranji.
Breast cancer is one of the most frequently occurring diseases which cause death among women. Masses present in mammogram of breast, primarily indicates breast cancer and it is important to classify them as benign or malignant. Benign and malignant masses differ in geometry and texture characteristics. However, not every geometry and texture feature that is extracted contributes to the improvement of classiﬁcation accuracy; thus, to select the best features from a set is important. Proposed new system will examine the feature selection methods for mass classification.
Pallavi P. Jadhav,Prof. U. A. Nuli (2017). Cancer Detection in Mammograms by Extracting Geometry and Texture Features. International Journal of Computer Engineering In Research Trends, 4(12), 552-555. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1203.pdf
: Breast cancer, mammograms, Region of Interest (ROI), Feature Extraction
 R. Rangayyan, N. Mudigonda, and J. Desautels, “Boundary modeling and shape analysis methods for classiﬁcation of mam-mographic masses,” Med. Biol. Eng. Comput., vol. 38, no. 5, pp. 487–496, 2000.R. Nicole, "The Last Word on Decision Theory," J. Computer Vision, submitted for publication. (Pending publication)
 N. Mudigonda, R. Rangayyan, and J. Desautels, “Gradient and texture analysis for the classiﬁcation of mammographic masses,” IEEE Trans.Med. Imag., vol. 19, no. 10, pp. 1032–1043, Oct. 2000.
 J. Kilday, F. Palmieri, and M. Fox, “Classifying mammographic lesionsusing computerized image analysis,” IEEE Trans. Med. Imag., vol. 12,no. 4, pp. 664–669, Dec. 1993.S.P. Bingulac, “On the Compatibility of Adaptive Controllers,” Proc. Fourth Ann. Allerton Conf. Circuits and Systems Theory, pp. 8-16, 1994. (Conference proceedings)
 S. Pohlman, K. Powell, N. Obuchowski, W. Chilcote, andS. Grundfest-Broniatowski, “Quantitative classiﬁcation of breast tumors in digitized mammograms,” Med. Phys., vol. 23, no. 8, pp. 1337–1345,Aug. 1996.
 A. Rojas Dominguez and A. Nandi, “Toward breast cancer diagnosis based on automated segmentation of masses in mammograms,”Pattern Recog., vol. 42, no. 6, pp. 1138–1148, Jun. 2009
 B. Sahiner, H. Chan, N. Petrick, M. Helvie, and M. Goodsitt, “Computerized characterization of masses on mammograms: The rubber bandstraightening transform and texture analysis,” Med. Phys., vol. 25, no. 4,pp. 516–526, Apr. 1998.
 M .MeselhyEltoukhy, I. Faye, and B. Belhaouari Samir, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram,”Comput. Biol. Med., vol. 40, no. 4, pp. 384–391, Apr. 2010.
 ] R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif.Intell., vol. 97, no. 1/2, pp. 273–324, Dec. 1997.
 I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classiﬁcation using support vector machines,” Mach. Learn., vol. 46,no. 1, pp. 389–422, 2002.
 P. A. Estévez, M. Tesmer, C. A. Perez, and J. M. Zurada, “Normalized mutual information feature selection,” IEEE Trans. Neural Netw., vol. 20,no. 2, pp. 189–201, Feb. 2009.
 P. A. Mundra and J. C. Rajapakse, “SVM-RFE with MRMR ﬁlter for gene selection,” IEEE Trans. NanoBiosci., vol. 9, no. 1, pp. 31–37,Mar. 2010
 H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, ”IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226–1238,Aug. 2005.