Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection

Rima Daoudi, Khalifa Djemal

2016

Abstract

We present in this work an Artificial Immune System (AIS) algorithm for breast cancer classification and diagnosis. The main contribution is to select memory cells according to their belonging to a validity interval based on average similarity of training cells. The behaviour of these created memory cells preserves the diversity of original cancer learning class. All these operations allow to generate a set of memory cells with a global representativeness of the database which enables breast cancer classification and recognition. Promising results have been obtained on both Wisconsin Diagnosis Breast Cancer Database (WDBC) and (DDSM) Digital Database for Screening Mammography.

References

  1. Arora, N., Martins, D., Ruggerio, D., Tousimis, E., Swistel, A. J., Osborne, M. P., and Simmons, R. M. (2008). Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer. The American Journal of Surgery, 196(4):523-526.
  2. Brownlee, J. (2005). Clonal selection theory & clonalg-the clonal selection classification algorithm (csca). Swinburne University of Technology.
  3. Burnet, S. F. M. et al. (1959). The clonal selection theory of acquired immunity. University Press Cambridge.
  4. Cheikhrouhou, I., Djemal, K., and Maaref, H. (2011). Protuberance selection descriptor for breast cancer diagnosis. In Visual Information Processing (EUVIP), 2011 3rd European Workshop on, pages 280-285. IEEE.
  5. Daoudi, R., Djemal, K., and Benyettou, A. (2013). An immune-inspired approach for breast cancer classification. In International Conference on Engineering Applications of Neural Networks, pages 273-281. Springer Berlin Heidelberg.
  6. Daoudi, R., Djemal, K., and Benyettou, A. (2014). Digital database for screening mammography classification using improved artificial immune system approaches. In 6th International Conference on Evolutionary Computation Theory and Applications (ECTA 2014) Part of the 6th International Joint Conference on Computational Intelligence (IJCCI 2014), pages 244-250.
  7. Daoudi, R., Djemal, K., and Benyettou, A. (2015). Improving cells recognition by local database categorization in artificial immune system algorithm. application to breast cancer diagnosis. In Evolving and Adaptive Intelligent Systems (EAIS), 2015 IEEE International Conference on, pages 1-6. IEEE.
  8. Dasgupta, D. and Majumdar, N. S. (2002). Anomaly detection in multidimensional data using negative selection algorithm. In wcci, pages 1039-1044. IEEE.
  9. De Castro, L. N. and Von Zuben, F. J. (2000). The clonal selection algorithm with engineering applications. In Proceedings of GECCO, volume 2000, pages 36-39.
  10. de Castro, L. N. and Von Zuben, F. J. (2001). ainet: an artificial immune network for data analysis. Data mining: a heuristic approach, 1:231-259.
  11. De Castro, L. N. and Von Zuben, F. J. (2002). Learning and optimization using the clonal selection principle. Evolutionary Computation, IEEE Transactions on, 6(3):239-251.
  12. Hagan, M. T., Demuth, H. B., Beale, M. H., and De Jesús, O. (1996). Neural network design, volume 20. PWS publishing company Boston.
  13. Heath, M. D. and Bowyer, K. W. (2000). Mass detection by relative image intensity. In Proceedings of the 5th International Workshop on Digital Mammography (IWDM-2000), pages 219-225.
  14. Jerne, N. K. (1974). Towards a network theory of the immune system. In Annales d'immunologie, volume 125, pages 373-389.
  15. Kachouri, I. C., Djemal, K., and Maaref, H. (2012). Characterisation of mammographic masses using a new spiculated mass descriptor in computer aided diagnosis systems. International Journal of Signal and Imaging Systems Engineering, 5(2):132-142.
  16. Marcano-Ceden˜o, A., Quintanilla-Domínguez, J., and Andina, D. (2011). Wbcd breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications, 38(8):9573- 9579.
  17. Neves, J., Guimara˜es, T., Gomes, S., Vicente, H., Santos, M., Neves, J., Machado, J., and Novais, P. (2015). Logic programming and artificial neural networks in breast cancer detection. In Advances in Computational Intelligence, pages 211-224. Springer.
  18. Sellami-Masmoudi, D., Maaref, H., Cheikhrouhou, I., Djemal, K., and Derbel, N. (2009). Empirical descriptors evaluation for mass malignity recognition. In First International Workshop on Medical Image Analysis and Description for Diagnosis Systems (MIAD 2009), pages 91-100.
  19. Sharma, A. and Sharma, D. (2011). Clonal selection algorithm for classification. In Artificial Immune Systems: 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings, volume 6825, page 361. Springer.
  20. Somayaji, A., Hofmeyr, S., and Forrest, S. (1998). Principles of a computer immune system. In Proceedings of the 1997 workshop on New security paradigms, pages 75-82. ACM.
  21. Tasnim, M., Rouf, S., and Rahman, M. S. (2014). A clonalg-based approach for the set covering problem. Journal of Computers, 9(8):1787-1795.
  22. Torrents-Barrena, J., Puig, D., Melendez, J., and Valls, A. (2015). Computer-aided diagnosis of breast cancer via gabor wavelet bank and binary-class svm in mammographic images. Journal of Experimental & Theoretical Artificial Intelligence, pages 1-17.
  23. Watkins, A., Timmis, J., and Boggess, L. (2004). Artificial immune recognition system (airs): An immuneinspired supervised learning algorithm. Genetic Programming and Evolvable Machines, 5(3):291-317.
  24. White, J. and Garrett, S. (2003). Improved pattern recognition with artificial clonal selection? Artificial Immune Systems, pages 181-193.
  25. Wolberg, W. H. and Mangasarian, O. L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proceedings of the national academy of sciences, 87(23):9193-9196.
  26. Yang, C.-H., Lin, Y.-D., Chaung, L.-Y., and Chang, H.- W. (2013). Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype snp barcodes. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 10(2):361-371.
  27. Zemmal, N., Azizi, N., Dey, N., and Sellami, M. (2016). Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. Journal of Medical Imaging and Health Informatics, 6(1):53-62.
  28. Zhang, Y. (2011). Distributed Intrusion Detection System in A Multi-Layer Network Architecture of Smart Grids. PhD thesis, The University of Toledo.
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Paper Citation


in Harvard Style

Daoudi R. and Djemal K. (2016). Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 209-216. DOI: 10.5220/0006057202090216


in Bibtex Style

@conference{ecta16,
author={Rima Daoudi and Khalifa Djemal},
title={Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006057202090216},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
SN - 978-989-758-201-1
AU - Daoudi R.
AU - Djemal K.
PY - 2016
SP - 209
EP - 216
DO - 10.5220/0006057202090216