Multi domain features based classification of mammogram images using SVMand MLP

ICIC Express Letters

Volumn 4, Issue 3, 2010, Pages 937-942

  Jaffar, A ^a   Ahmed, B ^a   Naveed, N ^a   Hussain, A ^a   Jabeen, F ^b   Mirza, A M ^a  

^a NATIONAL UNIVERSITY OF COMPUTER AND EMERGING SCIENCES   (Pakistan)

^b Allama Fqbal Open University   (Pakistan)

Author keywords

Breast cancer; Classification; Mammogram; Support vector machine

Indexed keywords

BREAST CANCER; CLASSIFICATION ,; MULTI DOMAINS; MULTI-LAYER PERCEPTION; REMOVAL METHOD; TUMOUR DETECTION;

ALGORITHMS; SUPPORT VECTOR MACHINES; X RAY SCREENS;

MAMMOGRAPHY;

EID: 77953901569     PISSN: 1881803X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (13)

1. M. Jankowski and J. Kuska, Connected components labelling algorithms inmathematica, Java, C++ and C#, IMS 2004, 2004.
2. C. J. Baines, D. V. McFarlane and A. B. Miller, The role of the referenceradiologist: Estimates of interobserver agreement and potential delay in cancerdetection in the national screening study, Investiga Radiol, vol.25,pp.971-976, 1990.
3. M. Wallis, M. Walsh and J. Lee, A review of false negative mammography ina ymptomatic population, Clin. Radiology, vol.44, pp.13-15, 1991.
4. N. A. Mat-Isa, M. Y. Mashor and N. H. Othman, Seeded region growingfeatures extraction algorithm; its potential use in improving screening forcervical cancer, International Journal of the Computer, the Internet and,Management, vol.13, pp.61-70, 2005.
5. M. Antonie, O. R. Zaiane and A. Coman, Application of data miningtechniques for medical image classification, Proc. of the 2nd, InternationalWorkshop on Multimedia Data, Mining, in Conjunction with ACM and, SIGKDDConference, San Francisco, 2001.
6. http://www.csie.ntu.edu.tw/cjlin/libsvm/.
7. http://skye.icr.ac.uk/m,ia,sd,b/m,ia,sd,b.htm,l.
8. J. Suckling et al., The mammographic image analysis society digitalmammogram database, Exerpta, Medico, International Congress Series 1069,pp.375-378, 1994.
9. R. M. Haralick, K. Shanmugan and I. Dinstein, Textural features for imageclassification, IEEE Transactions on System,s, Man, and, Cybernetics,vol.SMC-3, pp.610-621, 1973.
10. A. Hussain, M. A. Jaffar, A. M. Mirza and A. Chaudhry, Detail preservingfuzzy filter for impulse noise removal, International Journal of InnovativeComputing, Information and, Control, vol.5, no.10(B), pp.3583-3591, 2009.
11. M. A. Jaffar, A. Hussain, A. M. Mirza and A. Chaudhry, Fuzzy entropy andmorphology based fully automated segmentation of lungs from CT scan images,International Journal of Innovative Computing, Information and, Control, vol.5,no.12(B), pp.4993-5002, 2009.
12. T. Tateyama, Z. Nakao, X. Han and Y.-W. Chen, Contrast enhancement of MRbrain images by canonical correlations based kernel independent componentanalysis, International Journal of Innovative Computing, Information and,Control, vol.5, no.7, pp.1857-1866, 2009.
13. K. Yuan, F. Peng and W. Chen, Brain image preprocessing and application,International Journal of Innovative Computing, Information and, Control, vol.5,no.8, pp.2333-2340, 2009.