CFP last date
15 April 2024
Reseach Article

Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm

by Belgrana Fatima Zohra, Benamrane Nacera
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 3
Year of Publication: 2013
Authors: Belgrana Fatima Zohra, Benamrane Nacera
10.5120/ijais12-450669

Belgrana Fatima Zohra, Benamrane Nacera . Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm. International Journal of Applied Information Systems. 6, 3 ( October 2013), 1-5. DOI=10.5120/ijais12-450669

@article{ 10.5120/ijais12-450669,
author = { Belgrana Fatima Zohra, Benamrane Nacera },
title = { Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm },
journal = { International Journal of Applied Information Systems },
issue_date = { October 2013 },
volume = { 6 },
number = { 3 },
month = { October },
year = { 2013 },
issn = { 2249-0868 },
pages = { 1-5 },
numpages = {9},
url = { https://www.ijais.org/archives/volume6/number3/532-0669/ },
doi = { 10.5120/ijais12-450669 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:52:19.633370+05:30
%A Belgrana Fatima Zohra
%A Benamrane Nacera
%T Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 6
%N 3
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose an approach for detection of anomalies present in medical images. The idea is to combine tow metaphors: Neural Networks (NN) and Evolutionary Algorithm (EA) in a hybrid system. The Radial Basis Function Neural Network (RBF NN) and Multi Population Genetic Algorithm (MPGA) are coupled in one system called neural-evolutionary algorithm. After applying the growing region algorithm to extract regions, the RBF NN detects the suspect regions. Some of experimental results on mammographic images show the feasibility of the proposed approach.

References
  1. Kegelmeyer, W. P. Jr. , 1992. Computer detection of stellate lesions in mammograms," in Proc. SPIE Biomed. Image Processing, vol. 1660, pp. 446454.
  2. Dehmeshki, J. 2003. Automated Detection of Nodules in the CT Lung Images using Multi-Modal Genetic Algorithm, Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, pp. 393-398.
  3. Benamrane, N. , Freville, A. and Nekkahce, R. 2005. Hybrid fuzzy neural network for detection of tumors in medical images ", American Journal of Applied Sciences, 2(4) 892-896, ISSN 1546-9239.
  4. M. B. Bouchon, "Fuzzy Logique and its applications ", Edition Addison-Wesley, Paris, 1995.
  5. Nauck, D. 1994. A Fuzzy Perceptron as a Generic Model for Neuro- Fuzzy approaches, Proceedings of Fuzzy Systems and GI-Workshop, Ph. D. Thesis, Department of Computer Science, University of Braunshweig, Germany.
  6. Nauck, D. and Kruse, R. 1997. What are Neuro-Fuzzy Classifiers?, Proceedings of the Seventh International Fuzzy Systems Association World Congress, pp. 228-233.
  7. Lin, C. T. 1996 Neural Fuzzy System with Fuzzy Supervised Learning, IEEE Trans on System Man, and Cybernetics, Vol. 26(5),
  8. Benamrane, N. , Aribi, A. and Kraoula, L. 2006. Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation», International Conference on Geometric Modeling and Imaging GMAI06, Londres.
  9. Xin, Y. 1999. Evolving artificial neural networks, Proceeding of the IEEE, Vol 87, No 9. pp. 1423-1447.
  10. Laurikkala, J. and Juhola, M. 1999. Comparison of Genetics Algorithms in the Diagnosis Female Urinary Incontinence", Methods of Informatics in medicine, Ph. D. thesis, Department of Computer Science, University of Tampere, Finland.
  11. Sheifer, U. 2001 Multiple layer perceptrons training using genetic algorithms, Proceedings- European Symposium on Artificial Neural Networks, , ISBN 2-930307-01-3, pp. 159-164.
  12. Darken, M. 1989 Fast learning in networks of locally tuned processing units, In Neural Computations, vol. 1,pp. 281-294.
  13. Jiang, J. , Trundle, P. and Ren , J. 2010 Medical Image Analysis with Artificial Neural Networks, Digital Media & Systems Research Institute, University of Bradford, Bradford, BD7 1DP, United Kingdom.
  14. Davidor, Y. 1990 Genetic Algorithm and Robotics, World Scientific: an international publisher, New Jasey.
  15. Goldbag, D. E. 1989 Genetic Algofithmr in Searh, optimizatiom and Mochin Learning, Addison-Wesley publishing Company.
  16. McCulloch, W. S. and Pitts,W. 1943 A logical calculus of the ideas immanent in nervous activity", Bulletin of Mathematical Biophysics, , No 5, pp. 115–133.
  17. Grefenstette, J. J. 1992 Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144
  18. Goldberg, D. E. Genetics Algorithms in Search, Optimisation and Machine Learning, Addison Wesley
Index Terms

Computer Science
Information Sciences

Keywords

Tumor Detection Interpretation RBF NN MPGA Mammographic Images