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Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks

  • Rolando R. Hernández-Cisneros
  • Hugo Terashima-Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper proposes a procedure for the classification of microcalcification clusters in mammograms using sequential difference of gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward artificial neural network (ANN) trained with backpropagation. We found that the use of genetic algorithms (GAs) for finding the optimal weight set for an ANN, finding an adequate initial weight set before starting a backpropagation training algorithm and designing its architecture and tuning its parameters, results mainly in improvements in overall accuracy, sensitivity and specificity of an ANN, compared with other networks trained with simple backpropagation.

Keywords

Hide Layer Digital Mammogram Feature Selection Process Evolutionary Neural Network Microcalcification Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Thurfjell, E.L., Lernevall, K.A., Taube, A.A.S.: Benefit of independent double reading in a population-based mammography screening program. Radiology 191, 241–244 (1994)Google Scholar
  2. 2.
    Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  3. 3.
    Balakrishnan, K., Honavar, V.: Evolutionary design of neural architectures. A preliminary taxonomy and guide to literature. Technical Report CS TR 95-01, Department of Computer Sciences, Iowa State University (1995)Google Scholar
  4. 4.
    Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., Savage, J.: The Mammographic Images Analysis Society digital mammogram database. Exerpta Medica International Congress Series 1069, 375–378 (1994), http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html Google Scholar
  5. 5.
    Gulsrud, T.O.: Analysis of mammographic microcalcifications using a computationally efficient filter bank. Technical Report, Department of Electrical and Computer Engineering, Stavanger University College (2001)Google Scholar
  6. 6.
    Hong, B.-W., Brady, M.: Segmentation of mammograms in topographic approach. In: IEE International Conference on Visual Information Engineering, Guildford, UK (2003)Google Scholar
  7. 7.
    Li, S., Hara, T., Hatanaka, Y., Fujita, H., Endo, T., Iwase, T.: Performance evaluation of a CAD system for detecting masses on mammograms by using the MIAS database. Medical Imaging and Information Science 18(3), 144–153 (2001)MATHGoogle Scholar
  8. 8.
    Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Information Theory IT-8, 179–187 (1962)Google Scholar
  9. 9.
    Kozlov, A., Koller, D.: Nonuniform dynamic discretization in hybrid networks. In: Proceedings of the 13th Annual Conference of Uncertainty in AI (UAI), Providence, Rhode Island, USA, pp. 314–325 (2003)Google Scholar
  10. 10.
    Gupta, L., Srinath, M.D.: Contour sequence moments for the classification of closed planar shapes. Pattern Recognition 20(3), 267–272 (1987)CrossRefGoogle Scholar
  11. 11.
    Cantú-Paz, E., Kamath, C.: Evolving neural networks for the classification of galaxies. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002, San Francisco, CA, USA, pp. 1019–1026 (2002)Google Scholar
  12. 12.
    Skinner, A., Broughton, J.Q.: Neural networks in computational material science: traning algorithms. Modeling and Simulation in Material Science and Engineering 3, 371–390 (1995)CrossRefGoogle Scholar
  13. 13.
    Oporto-Díaz, S., Hernández-Cisneros, R.R., Terashima-Marín, H.: Detection of microcalcification clusters in mammograms using a difference of optimized gaussian filters. In: Kamel, M., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 998–1005. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rolando R. Hernández-Cisneros
    • 1
  • Hugo Terashima-Marín
    • 1
  1. 1.Center for Intelligent SystemsTecnológico de MonterreyMonterrey, Nuevo LeónMexico

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