Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches

Rima Daoudi, Khalifa Djemal, Abdelkader Benyettou

2014

Abstract

Breast cancer ranks first in the causes of cancer deaths among women around the world. Early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Mammography is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In this aim, Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research, the purpose of this resource is to provide a large set of mammograms in a digital format. DDSM has been widely used by researchers to evaluate different computer-aided algorithms such as neural networks or SVM. The Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system, they are able of learning, memorize and perform pattern recognition. We propose in this paper several enhancements of CLONALG algorithm, one of the most popular algorithms in the AIS field, which are applied on DDSM for breast cancer classification using adapted descriptors. The obtained classification results are 98.31% for CCS-AIS and 97.74% for MF-AIS against 95.57% for original CLONALG. This proves the effectiveness of the used descriptors in the two improved techniques.

References

  1. Ferlay J, and al., 2013. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer.Available from http://globocan.iarc.fr
  2. Marcano-Cedeno, A., J. Quintanilla-Dominguez, and D. Andina,2011,WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications, 38(8): pp. 9573-9579.
  3. Timmy Manning and Paul Walsh, 2013.Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions, in Proceeding of 11th European Conference, EvoBIO 2013, Vienna, Austria.
  4. Hossein Ghayoumi Zadehand al.,2012.Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging, Iranian Journal of Medical Physics Vol. 9, No. 4, pp 265-274.
  5. Aboul Ella Hassanien and al. 2014. MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier, Elsevier Applied Soft Computing , Volume 14 , pp 62-71
  6. Mahnaz Rafie and Ali Broumandnia, 2013.Evaluation of Cancer Classification Using Combined Algorithms with Support Vector Machines, International Journal of Computer & Information Technologies (IJOCIT13), Vol 1, Issue 2 , pp 137-148
  7. Aboul Ella Hassanien and al.2013.Breast Cancer Detection and Classification Using Support Vector Machines and Pulse Coupled Neural Network, in Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, Advances in Intelligent Systems and Computing Volume 179, 2013, pp 269- 279
  8. Jain, R. and J. Mazumdar, 2003. A genetic algorithm based nearest neighbor classification to breast cancer diagnosis. Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine, 26(1): p. 6-11.
  9. Mazurowski, M.A., et al.2007. Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms. 2007 Ieee Congress on Evolutionary Computation, Vols 1-10, Proceedings2007. 600-605.
  10. Wan Noor Aziezan Baharuddin and al. 2013. MamdaniFuzzy Expert System for BIRADS Breast Cancer Determination Based on Mammogram Images, Springer Soft Computing Applications and Intelligent Systems Communications in Computer and Information Science Volume 378, pp 99-110
  11. Anuarg Sharma and Dharmendra Sharma, 2011. Clonal Selection Algorithm for Classification , in Proceeding of 10th International Conference on Artificial Immune Systems ICARIS 2011, Vol 6825 of LNCS,(pp. 361- 370). Springer.
  12. Rima Daoudi, Khalifa Djemal and Abdelkader Benyettou, 2013a. Cells clonal selection for Breast Cancer classification, in proceeding of 10th International Multi-Conference on Systems, Signals & Devices SSD13.
  13. Rima Daoudi, Khalifa Djemal and Abdelkader Benyettou. 2013b. An Immune-Inspired Approach for Breast Cancer Classification, in proceeding of 14th International Conference on Engineering Applications of Neural Networks EANN 2013, Series Volume 38, pp 273-281, Springer.
  14. Leung, K., Cheong, F., Cheong, C.2007. Generating compact classifier systems using a simple artificial immune system. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 37(5), 1344-1356 (2007)
  15. J. H. Ang, K. C. Tan, A. A. Mamum, 2010. An evolutionary memetic algorithm for rule extraction, Expert Systems with Applications 37 (2010) 1302- 1315
  16. Jerne, N.K., 1974, Towards a Network Theory Of Immune System. Annales D Immunologie,.C125(1-2): p. 373-389.
  17. Uwe Aickelin, Dipankar Dasgupta, Feng Gu,2014. Artificial Immune Systems, Search Methodologies, Springer Science+Business Media New York, pp 187- 211,
  18. De Castro, L. N.; Von Zuben, F. J,2002.Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems (IEEE) , 2002. 6. (3): 239-251.
  19. M. Heath, K. W. Bowyer, D. Kopans, et al. 2002. The Digital Database for Screening Mammography, presented at 5th International Workshop on Digital Mammography Toronto, Canada, 2000.
  20. Imene Cheikhrouhou Kachouri, 2012, Description et classification des masses mammaires pour le diagnostic du cancer du sein, Ph.D. Thesis. Uiversity of Evry Val d'Essone: France.
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Paper Citation


in Harvard Style

Daoudi R., Djemal K. and Benyettou A. (2014). Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 244-250. DOI: 10.5220/0005079602440250


in Bibtex Style

@conference{ecta14,
author={Rima Daoudi and Khalifa Djemal and Abdelkader Benyettou},
title={Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={244-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005079602440250},
isbn={978-989-758-052-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches
SN - 978-989-758-052-9
AU - Daoudi R.
AU - Djemal K.
AU - Benyettou A.
PY - 2014
SP - 244
EP - 250
DO - 10.5220/0005079602440250