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Reseach Article

Implementation of Neural Network for Cancer Diagnosis

Published on November 2011 by Devesh D. Nawgaje, Dr. Rajendra D.Kanphade
2nd National Conference on Information and Communication Technology
Foundation of Computer Science USA
NCICT - Number 3
November 2011
Authors: Devesh D. Nawgaje, Dr. Rajendra D.Kanphade
e77b3ee3-7efc-41c1-b69b-3012a47a7c8d

Devesh D. Nawgaje, Dr. Rajendra D.Kanphade . Implementation of Neural Network for Cancer Diagnosis. 2nd National Conference on Information and Communication Technology. NCICT, 3 (November 2011), 19-24.

@article{
author = { Devesh D. Nawgaje, Dr. Rajendra D.Kanphade },
title = { Implementation of Neural Network for Cancer Diagnosis },
journal = { 2nd National Conference on Information and Communication Technology },
issue_date = { November 2011 },
volume = { NCICT },
number = { 3 },
month = { November },
year = { 2011 },
issn = 0975-8887,
pages = { 19-24 },
numpages = 6,
url = { /proceedings/ncict/number3/4290-ncict020/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Information and Communication Technology
%A Devesh D. Nawgaje
%A Dr. Rajendra D.Kanphade
%T Implementation of Neural Network for Cancer Diagnosis
%J 2nd National Conference on Information and Communication Technology
%@ 0975-8887
%V NCICT
%N 3
%P 19-24
%D 2011
%I International Journal of Computer Applications
Abstract

Cancer is the term used for diseases in which abnormal cells divide without control and are able to invade other tissues. There are more than hundred different types of cancer, one of them being Breast Cancer. Percentage of population, dying due to this type of cancer or due to incorrect diagnosis is very high. This invokes the idea of use of some Artificial Intelligence techniques for detection of this type of cancer. One technical approach is to use Image Processing as initial step in detecting, followed by suitable artificial intelligent techniques. Image processing basically involves number of processes, out of which feature extraction satisfied our need. Some of the Image Processing parameters have been extracted from mammographic images using MATLAB. These parameters served as input for training Neural-Network. An unknown image was then taken to test the trained system.

References
  1. D.D.Nawgaje, Dr.R.D.Kanphade, S.B.Patil, “Evolutionary Computing Techniques For Cancer Diagnosis : Review”, Internaltional Journal on Computer Engineering and Information Technology, ISSN 0974-2034, Vol 9, No. 14, pp 7-11, Jan2010
  2. Devesh D.Nawgaje, Dr.Rajendra D.Kanphade, “Implementation of ANFIS for Breast Cancer Detection using TMS320C6713 DSP”, International Journal of Computer Applications, No. 13, Article 2, 2011.
  3. Keyvanfard, F.; Shoorehdeli, M.A.; Teshnehlab, M.; “Feature selection and classification of breast MRI lesions based on Multi classifier”, IEEE, Artificial Intelligence and Signal Processing (AISP), 2011 , Page(s): 54 – 58
  4. Dheeba, J.; Tamil Selvi, S.; “Screening mammogram images for abnormalities using radial basis Function Neural Network”, IEEE, Communication Control and Computing Technologies (ICCCCT), 2010, Page(s): 554 – 559.
  5. Ashraf, M.; Kim Le; Xu Huang; “Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses”, IEEE, Computer Sciences and Convergence Information Technology (ICCIT), 2010, Page(s): 911 – 915.
  6. Osareh, A.; Shadgar, B.;” Machine learning techniques to diagnose breast cancer”, IEEE Health Informatics and Bioinformatics (HIBIT), 2010 , Page(s): 114 - 120 N
  7. L. Shen, R. Rangayyan, and J. Desaultels, Detection and Classification Mammographic Calcifications, International Journal of Pattern Recognition and Artificial Intelligence. Singapore: World Scientific, 1994, pp. 1403–1416.
  8. F. Aghdasi, R.Ward, and B. Palcic, “Restoration of mammographic images in the presence of signal-dependent noise,” in State of the Art in Digital Mammographic Image Analysis. Singapore: World Scientific, 1994, vol. 7, pp. 42– 63.
  9. Y. Chitre, A. Dhawan, and M. Moskowtz, “Artificial neural network based classification of mammographic microcalcifications using image structure features,” in State of the Art of Digital Mammographic Image Analysis. Singapore: World Scientific, 1994, vol. 7, pp. 167–197.
  10. E. Pisano and F. Shtern, “Image processing and computer- aided diagnosis in digital mammography,” in State of the Art of Digital Mammographic Image Analysis. Singapore:World Scientific, 1994, vol. 7, pp. 280–291.
  11. K. Bowyer and S. Astley, The Art of Digital Mammographic Image. Singapore: World Scientific, 1994, vol. 7, p. v.
  12. K.Woods, C. Doss, K. Bowyer, J. Solka, C. Priebe, andW. Kegelmeyer, “Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography,” Int. J. Pattern Recog. Artif. Intell., vol. 7, no. 6, pp. 1417–1436, 1994.
  13. R. Nishikawa, M. Giger, K. Doi, C. Vyborny, and R. Schmidt, “Computer- aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms,” in State of the Art of Digital Mammographic Image Analysis. Singapore: World Scientific, 1994, vol. 7, pp. 82–102.
  14. H. Yoshida, R. Nishikawa, K. Muto, K. Doi, and M. Tsuda, “Application of the wavelet transform to automated detection of clustered microcalcifications in digital mammograms,” Tokyo Inst. Polytech., Tokyo, Japan, Academic Rep., vol. 16, 1994.
  15. H. Yoshida, R. Nishikawa, G. Maryellen, and K. Doi, “Computer-aided diagnosis in mammography: Detection of clustered microcalcifications based on multiscale edge representation,” in Computer Assisted Radiology. Amsterdam, The Netherlands: Elsevier, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer classification digital mammograms microcalcification neural networks