References

65

References

1. Cancer Facts and Figures 2010 [online]. Available:

http://www.cancer.org/Research/ CancerFactsFig-ures/cancer-facts-and-figures-

2010.

2. Tawam's 2012 Breast Cancer Awareness Campaign [online]. Available:http://

www.ameinfo.co-m/ tawams-2012-breast-cancer-awareness-campaign-312992.

3. Male Breast Cancer[online]. Available:http://www.nlm.nih.gov/medlineplus

/malebreastcancer.html.

4. G. I. Salama, M. B. Abdelhalim, and M. A. Zeid, “Breast Cancer Diagnosis on

Three Different Datasets Using Multi-Classifiers”, Int. J. of Comput. and Inform.

Technology, vol. 1, no. 1, pp. 2277 – 0764 , Sept. 2012.

5. http://www.beliefnet.com/healthandhealing/getcontent.aspx?cid=21322

6. Research Activities January 2012[online]. Available: http://www.ahrq.gov

/research/jan12/0112RA-20.htm.

7. Merck Manual of Diagnosis and Therapy (February 2003). "Breast Disorders:

Breast Cancer". retrieved 2008-02-05.

8. WatsonM(2008)."Assessment of suspected cancer". Innoait 1 (2): 94 107.doi:10.

1093/innovait/inn001.

9. National Cancer Institute (27 June, 2005). "Paget's Disease of the Nipple:

Questions and Answers". Retrieved 2008-02-06.

10. Breast Cancer [online]. Available: http://www.womenshealth.gov/ breast-

cancer/what-is-breast-cancer.

11. O.L. Mangasarian, W. N. Street, W. H. Wolberg, Breast cancer diagnosis and

prognosis via linear programming. Mathematical Programming Technical report,

43(4), pages 94-10, Dec 1994.

12. S. W. Fletcher, W. Black, R. Harris, B. K. Rimer, and S. Shapiro. Report of the

international work shop on screening for breast cancer. Journal of the National

Cancer Institute, 85: 1644-1656,1993.

66

13. R. W. M. Giard and J. Hermans. The value of aspiration cytologic examination of

the breast. A statistical review of the medical literature. Cancer, 69:2104-2110,

1992.

14. Patrick Pantel, Breast Cancer Diagnosis and Prognosis.

15. P. L. Choong and C. J. S. deSilva, “Breast Cancer Prognosis using the EMN

Architecture”, 1994 IEEE International Conference on Neural Networks, Orlando,

Florida, 1994.

16. E. T. Lee. Statistical Methods for Survival Data Analysis. Joha Wiley and Sons,

New York, 1992.

17. M. M. Beg and M. Jain, “An Analysis of the methods employed for breast cancer

diagnosis”, Int. J. of Research in Comput. Sci., vol. 2, no. 3, 2012, pp. 25-29.

18. T. Kiyan, T Yildirim , “Breast cancer diagnosis using statistical neural

networks”, J. of Elect. & Electron. Eng., vol .4-2, 2004, pp. 1149- 1153.

19. G. Schwarzer, W. Vach and M. Schumacher, 2000. On the misuses of artificial

neural networks for prognostic and diagnostic classification in oncology. Stat.

Med., 19: 541-561. PMID: 10694735

20. S. S. Haykin, Neural Networks and Learning Machines. 3rd Edn., Prentice Hall,

New York, 2009, pp. 906.

21. S. A. Mojarad, S. S. Dlay, W. L. Woo and, G. V. Sherbet “Cross Validation

Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural

Networks”, American J. of Engineering and Applied Sciences, vol. 5, no. 1, pp.

42-51, 2012.

22. F. Paulin and A. Santhakumaran, “Classification of breast cancer by comparing

back propagation training algorithms”, Int. J. on Comput. Sci. and Eng. (IJCSE),

vol. 3 no. 1, Jan 2011.

23. R. Nithya and B. Santhi, “Classification of Normal and Abnormal Patterns in

Digital Mammograms for Diagnosis of Breast Cancer” Int. J. of Comput.

Applicat., vol. 28, no.6, pp. 0975 – 8887, Aug. 2011.

24. J. H. Song, S. S. Venkatesh, E. F. Conant, T. W. Cary, P. H. Arger, C. M. Sehgal,

“Artificial neural network to aid differentiation of malignant and benign breast

masses by ultrasound imaging.”

67

25. Shirin A. Mojarad, Satnam S. Dlay, Wai L. Woo and, Gajanan V. Sherbet “Cross

Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron

Neural Networks” American J. of Engineering and Applied Sciences 5 (1) : 42-

51, 2012 ISSN 1941-7020.

26. Marcano-Cedeño , J. Quintanilla-Domínguez, D. Andina “WBCD breast cancer

database classification applying artificial metaplasticity neural network”, Expert

Systems with Applications 38 (2011) 9573–9579

27. F. Paulin and A. Santhakumaran, “Classification of breast cancer by comparing

back propagation training algorithms”, Int. J. on Comput. Sci. and Eng. (IJCSE),

vol. 3 no. 1, Jan 2011.

28. M. R. Senapati, P. K .Routray, P. K. Dask “Breast Cancer Detection using

Recursive Least Square and Modified Radial Basis Functional Neural Network”,

Int. Conf. [ICCT-2010], IJCCT vol. 2, no. 2, 3, 4, 3rd-5th December 2010.

29. S. Haykin, Neural Networks: A Comprehensive Foundation, Mac Millan College

Publishing Company, 1994.

30. D. Broomhead and D.Lowe, “Multivariable Functional Interpolation and adaptive

networks”, complex systems, vol. 2, pp. 321-355, 1998.

31. M. R. Senapati, P. K .Routray, P. K. Dask “Breast Cancer Detection using

Recursive Least Square and Modified Radial Basis Functional Neural Network”,

Int. Conf. [ICCT-2010], IJCCT vol. 2, no. 2, 3, 4, 3rd-5th December 2010.

32. Y. Shan, R. Zhao, G. Xu, H.M. Liebich, Y. Zhang, “Application of probabilistic

neural network in the clinical diagnosis of cancers based on clinical chemistry

data”, Analytica Chimica Acta, vol. 471, no. 1, pp. 77-86, Oct. 23, 2002.

33. H. Temurtas, N. Yumusak , F. Temurtas , “A comparative study on diabetes

disease diagnosis using neural networks”, Expert Systems with Applications, vol.

36, no. 4, pp. 8610-8615, May 2009.

34. G.S. Gill and J. S. Sohal, “Battlefield Decision Making: A Neural Network

Approach”, J. of Theoretical and Applied Inform. Technology, vol. 4, no. 8, pp.

697 –699, 2009.

68

35. S. N. Sivandam, S. Sumathi, and S. N. Deepa, Introduction to Neural Networks

using Matlab 6.0, TMH Company Ltd., 2006.

36. D. F. Specht, “Probabilistic Neural Networks”, Neural Networks, vol. 3, no. 1, pp.

109-118, 1990.

37. Ioannis Anagnostopoulos, Christos Anagnostopoulos, Angelos Rouskas, George

Kormentzas, Dimitrios Vergados “The Wisconsin Breast Cancer Problem:

Diagnosis and DFS time prognosis using probabilistic and generalised regression

neural classifiers”, Draft version of paper to appear at the Oncology Reports,

special issue Computational Analysis and Decision Support Systems in Oncology,

last quarter 2005.

38. C. Lu, J. De Brabanter, S. V. Huffel, I. Vergote, D. Timmerman, “Using artificial

neural networks to predict malignancy of ovarian tumors”, in Proc. 23rd Annual

Int. Conf. of the IEEE Eng. in Medicine and Biology Society, 2001, vol. 2, pp.

1637-1640.

39. R. G. Ahangar, M. Yahyazadehfar, and H. Pournaghshband, “The comparison of

methods artificial neural network with linear regression using specific variables

for prediction stock price in Tehran stock exchange”, Int. J. of Comput. Sci. and

Inform. Security (IJCSIS), vol. 7, no. 2, Feb. 2010.

40. I. Anagnostopoulos, C. Anagnostopoulos, A. Rouskas, G. Kormentzas and D.

Vergados “The wisconsin breast cancer problem: diagnosis and DFS time

prognosis using probabilistic and generalised regression neural classifiers” Draft

version of paper to appear at the Oncology Reports, special issue Computational

Analysis and Decision Support Systems in Oncology, last quarter 2005.

41. K. Rahul, S. Anupam and T. Ritu, “Fuzzy Neuro Systems for Machine Learning

for Large Data Sets,” Proc. of the IEEE Int. Adv. Computing Conf. 6-7, Patiala,

India, pp.541-545, 2009.

42. C. Juang, R. Huang and W. Cheng, “An interval type-2 fuzzy-neural network with

support-vector regression for noisy regression problems,” IEEE Trans. on Fuzzy

Systems, vol. 18, no. 4, pp. 686 – 699, 2010.

69

43. C. Juang, Y. Lin and C. Tu, “Recurrent self-evolving fuzzy neural network with

local feedbacks and its application to dynamic system processing,” Fuzzy Sets

and Systems, vol. 161, no. 19, pp. 2552-2562, 2010.

44. RR Yager, LA. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing. New

York: Van Nostrand Reinhold, 1994.

45. Carlos Andre´s Pen˜a-Reyes, Moshe Sipper “A fuzzy-genetic approach to breast

cancer diagnosis”, Artificial Intelligence in Medicine, vol. 1, no 2, pp. 131-155,

Oct. 1999.

46. I. Dumitrache, Ingineria Reglarii Automate (Automatic Control Engineering),

Bucuresti, Politehnica Press, 2005.

47. Carlos Andre´s Pen˜a-Reyes, Moshe Sipper “A fuzzy-genetic approach to breast

cancer diagnosis”, Artificial Intelligence in Medicine, vol. 1, no 2, pp. 131-155,

Oct. 1999.

48. Essam Al-Daoud “Cancer Diagnosis Using Modified Fuzzy Network”, Universal

Journal of Computer Science and Engineering Technology 1 (2), 73-78, Nov.

2010. © 2010 UniCSE, ISSN: 2219-2158.

49. Yuan-Hsiang Chang, BinZheng, Xiao-Hui Wang, Walter F. Good “Computer-

aided diagnosis of breast cancer using artificial neural networks: Comparison of

Backpropagation and Genetic Algorithms”, in Int. Joint Conf Neural Networks

UCNN’ 99, 1999, vol. 5, pp. 3674-3679.

50. R. R. Janghel, A. Shukla, R. Tiwari, and R. Kala, “Breast cancer diagnostic

system using symbiotic adaptive neuro-evolution (SANE)” in Proc. Int. conf. of

Soft Computing and Pattern Recognition 2010 (SoCPaR-2010), ABV-IIITM,

Gwalior, 7th-10th Dec., pp. 326-329.

51. M. Ashraf, L. Kim and H. Xu., “Information gain and adaptive neuro-fuzzy

inference system for breast cancer diagnoses”, in Proc. Comput. Sci. Convergence

Inform. Tech. 2010 (ICCIT-2010), IEEE, Seoul , 30th Nov.-2nd Dec., pp. 911 –

915.

52. V. Bevilacqua, G. Mastronardi, F. Menolascina “Hybrid data analysis methods

and artificial neural network design in breast cancer diagnosis: IDEST

experience”, in Proc. Int. Conf. on Intelligent Agents, Web Technologies and

70

Internet Commerce and Int. Conf. on Computational Intelligence for Modeling,

Control Automation 2005 (CIMCA-2005), 28th-30th Nov., IEEE, Vienna, pp.

373-378.

53. W. Land, and E. Veheggen, “Experiments using an evolutionary programmed

neural network with adaptive boosting for computer aided Diagnosis of breast

cancer”, in Proc. IEEE Int. Workshop on Soft Computing in Ind. Application,

2003 (SMCia-2003), Finland, 23rd-25th June, pp. 167-172.

54. P. Meesad and G. Yen., “Combined numerical and linguistic knowledge

representation and Its application to medical diagnosis”, IEEE transactions on

Syst., Man and Cybernetics, Part A: Syst. and Humans, vol. 3, no.2, pp. 206-222.

55. G. Arulampalam, and A. Bouzerdoum, “Application of shunting inhibitory

artificial neural networks to medical diagnosis”, in Proc. 7th Australian and New

Zealand Intelligent Inform. System Conf. 2001, IEEE, Perth, 18th-21st Nov.,

2001, pp. 89 -94.

56. A. Ismail “Training and optimization of product unit neural networks”, M.S.

Thesis, University of Pretoria, Pretoria, 2001

57. Gorman RP, Sejnowski TJ. Analysis of hidden units in a layered network trained

to classify sonar targets. Neural Networks 1988;1:75–89.

58. O’Neill M. Training back-propagation neural networks to define and detect DNA-

binding sites. Nucl Acids Res 1991;19:313–8.

59. Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins

using neural network models. J Mol Biol 1988;202:865–84.

60. White H. Learning in artificial neural networks: a statistical approach. Neural

Comput 1989;1:425–64.

61. “Principles of Soft Computing”, Book by Dr. S.N. Sivanandam, S.N. Deepa,

First Indian Edition, Wiley Publication, 2008.

62. R. Rojas. Neural Networks: a systematic introduction. Springer-Verlag, 1996.

63. Dr. K. U. Rani “Parallel Approach for Diagnosis of Breast Cancer using Neural

Network Technique”, International Journal of Computer Applications (0975 –

8887), Volume 10– No.3, November 2010

71

64. G. K. Jha, Artificial neural networks and its applications [online]. Available:

www.iasri.res.in/ebook /EBADAT/5.../5-ANN_GKJHA_2007.pdf

65. Ruth M. Ripley, “Neural network models for breast cancer prognosis”, Ph. D.

Thesis, Dept. of Eng. and Sci., St. Cross College, Univ. Of Oxford, 1998.

66. F. Paulin and A. Santhakumaran, “Classification of breast cancer by comparing

back propagation training algorithms”, Int. J. on Comput. Sci. and Eng. (IJCSE),

vol. 3 no. 1, Jan 2011.

67. Simon Haykin. Neural Networks – A Comprehensive Foundation. Pearson

Education, 2001.

68. Lucila Ohno-Machado “ Medical applications of Artificial Neural Networks:

connectionist models of survival”, Ph.D. dissertation, submitted to the program

in medical information sciences and the committee on graduate studies, Stanford

University, March 1996.

69. G. I. Salama, M. B. Abdelhalim, and M. A. Zeid , “Breast Cancer Diagnosis on

Three Different Datasets Using Multi- Classifiers”, Int. J. of Comput. and

Inform. Technology, vol. 1, no. 1, pp. 2277 – 0764 , Sept. 2012.

70. Y. H. Pao, Adaptive Pattern Recognition and Neural Network, Addison-Wesley

Publishing Company, 1989.

71. P. Heermann and N. Khazenie,“Classifcation of multispectral remote sensing

data using a back- propagation neural network”, IEEE Trans. on Geoscience and

Remote Sensing, vol. 30, pp. 81-88 , 1992.

72. L. Giles and T. Maxwell, “Learning, invariance and generalization in highorder

neural networks”, In Applied Optics, Vol. 26, No. 23, Optical Society of America,

Washington DC, Pages 4972-4978, 1987.

73. Y. Pao. “Adaptive Pattern Recognition and Neural Networks.” Addison- Wesley,

USA, 1989. ISBN: 0 201012584-6

74. K. Burse, R. N. Yadav, S. C. Srivastava, V. P. S. Kirar, “A Compact Pi Network

for Reducing Bit Error Rate in Dispersive FIR Channel Noise Model,” Int. J. of

Elect. and Comput. Eng., vol. 3, no. 3, 2009, psp. 150-153.

72

75. R.N. Yadav, P.K. Kalra, and J. John, “Time series prediction with single

multiplicative neuron model”, Applied Soft Computing, vol. 7, pp. 1157-1163,

2007.

76. Baxt WG. Application of neural networks to clinical medicine. Lancet

1995;346:1135–8.

77. K. Anil Jain, Jianchang Mao and K.M. Mohiuddin. Artificial Neural Networks: A

Tutorial. IEEE Computers, 1996, pp.31-44.

78. George Cybenko. Neural Networks in Computational Science and Engineering.

IEEE Computational Science and Engineering, 1996, pp.36-42.

79. K. Papik et al. (1998), Application of neural networks in medicine- a review

[Online].Available:uran.donetsk.ua/~masters/2006/kita/zbykovsky/library/

nninmed.pdf.

80. R. W. Sembiring and J. M. Zain “The Design of Pre-Processing Multidimensional

Data Based on Component Analysis”, Comput. and Inform. Sci., vol. 4, no. 3, pp.

106-115, May 2011.

81. P. Cunningham, “Dimension Reduction”, August 2007.

82. M. Dash and H. Liu, “Dimensionality Reduction”, Wiley Encyclopedia of

Computer Science and Engineering, 2008.

83. G. I. Salama, M. B. Abdelhalim, and M. A. Zeid , “Breast Cancer Diagnosis on

Three Different Datasets Using Multi- Classifiers”, Int. J. of Comput. and

Inform. Technology, vol. 1, no. 1, pp. 2277 – 0764 , Sept. 2012.

84. A. Ilin and T. Raiko, “Practical Approaches to Principal Component Analysis in

the Presence of Missing Values”, J. of Machine Learning Research , vol. 11, pp.

1957-2000, 2010.

85. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao, “Statistical Pattern

Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence,

Vol. 22, No.1, January 2000.

86. Tang F., Tao H., Fast linear discriminant analysis using binary bases, Proc. of the

18th International Conference on Pattern Recognition (ICPR’06), (2006).

87. Brain D. Hahn and Daniel T. Valentine “Essential MATLAB for Engineers and

Scientists”, Third edition. Published by Elsevier, India, 2007.

73

88. C Xenophontos. A Beginner's Guide to  MATLAB [online], Clarkson University,

1999 , Available: web2.clarkson.edu/class/ma571/Xeno-MATLAB_guide.pdf.

89. Stephen J. Chapman, Essentials of MATLAB Programming. 2nd ed., Cengage

Learning, 2004.

90. Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth (1992).Neural

Network Toolbox™ User’s Guide[online]. Available:

www.mathworks.in/help/pdf_doc/nnet/nnet_ug.pdf.

91. A. Frank and A. Asuncion (2010). UCI Machine Learning Repository[Online].

Available: http://archive.ics.uci.edu/ml. Irvine, CA: University of California,

School of Information and Computer Science.

92. W. N. Street, W. H. Wolberg, and O. L. Mangasarian, “Nuclear feature extraction

for breast tumor diagnosis”, Proc. IS&T/ SPIE Int. Symp. on Electron. Imaging:

Sci. and Technology, 1993, vol. 1905, pp. 861–870.

93. M. Karabatak, M. C. Ince, “An expert system for detection of breast cancer based

on association ruled and neural network”, Expert System with Applications, vol.

36, pp. 3465-3469, 2009.

74