Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


February Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the February 2021 Edition of the journal. The last date of research paper submission is January 15, 2021.

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

Belgrana Fatima Zohra, Benamrane Nacéra Published in Signal Processing

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
Download full text
  1. Belgrana Fatima Zohra and Benamrane Nacera. Article: Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm. International Journal of Applied Information Systems 6(3):1-5, October 2013. BibTeX

    	author = "Belgrana Fatima Zohra and Benamrane Nacera",
    	title = "Article: Detection of Tumor in Mammographic Images by RBF Neural Network and Multi Population Genetic Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 6,
    	number = 3,
    	pages = "1-5",
    	month = "October",
    	note = "Published by Foundation of Computer Science, New York, USA"


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.


  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


Tumor Detection, Interpretation, RBF NN, MPGA Mammographic Images