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PIER PIER B PIER C PIER M PIER Letters
2011-02-14
Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast
By
Martin O'Halloran,

    Dr. Martin O'Halloran

    Electronic and Electrical Engineering

    National University of Ireland Galway

    Ireland

    Homepage

Brian McGinley,

    Dr. Brian McGinley

    College of Engineering and Informatics

    National University of Ireland Galway

    Ireland

    Homepage

Raquel Cruz Conceicao,

    Dr. Raquel Cruz Conceicao

    Faculdade de Ciências - Universidade de Lisboa

    Instituto de Biofísica e Engenharia Biomédica

    Portugal

    Homepage

Fearghal Morgan,

    Dr. Fearghal Morgan

    College of Engineering and Informatics

    National University of Ireland Galway

    Ireland

    Homepage

Edward Jones,

    Dr. Edward Jones

    Electronic and Electrical Engineering

    National University of Ireland Galway

    Ireland

    Homepage

Martin Glavin

    Dr. Martin Glavin

    Electronic and Electrical Engineering

    National University of Ireland Galway

    Ireland

    Homepage

Progress In Electromagnetics Research, Vol. 113, 413-428, 2011
doi:10.2528/PIER10122203
Abstract
The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be influenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifier is compared to an existing LDA classifier. The effect of combining conflicting classification readings in a multi-antenna system is also considered. Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed.
Citation
Martin O'Halloran, Brian McGinley, Raquel Cruz Conceicao, Fearghal Morgan, Edward Jones, and Martin Glavin, "Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast," Progress In Electromagnetics Research, Vol. 113, 413-428, 2011.
doi:10.2528/PIER10122203
References

1. Nass, S. L., I. C. Henderson, and J. C. Lashof, Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer , National Academy Press, 2001.

2. Bird, R. E., T. W. Wallace, and B. C. Yankaskas, "Analysis of cancers missed at screening mammograph," Radiology, Vol. 184, 613-617, 1992.

3. Huynh, P. H., A. M. Jarolimek, and S. Daye, "The false-negative mammogram," RadioGraphics, Vol. 18, 1137-1154, 1998.

4. Elmore, J. G., M. B. Barton, V. M. Moceri, S. Polk, P. J. Arena, and S. W. Fletcher, "Ten-year risk of false positive screening mammograms and clinical breast examinations," New Eng. J. Med., Vol. 338, No. 16, 1089-1096, 1998.
doi:10.1056/NEJM199804163381601

5. Hall, F. M., J. M. Storella, D. Z. Silverstone, and G. Wyshak, "Non-palpaple breast-lesions, recommendations for biopsy based on suspicion of carcinoma at mammography ," Radiology, Vol. 167, No. 2, 353-358, 1988.

6. Zainud-Deen, S., W. Hassen, E. Ali, and K. Awadalla, "Breast cancer detection using a hybrid finite difference frequency domain and particle swarm optimization techniques," Progress In Electromagnetics Research B, Vol. 3, 35-46, 2008.
doi:10.2528/PIERB07112703

7. AlShehri, S. and S. Khatun, "UWB imaging for breast cancer detection using neural network," Progress In Electromagnetics Research C, Vol. 7, 79-93, 2009.
doi:10.2528/PIERC09031202

8. Maskooki, A., E. Gunawan, C. Soh, and K. Low, "Frequency domain skin artifact removal method for ultra-wideband breast cancer detection ," Progress In Electromagnetics Research, Vol. 98, 299-314, 2009.
doi:10.2528/PIER09101302

9. Conceicao, R. C., D. Byrne, M. O'Halloran, M. Glavin, and E. Jones, "Comparison of planar and circular antenna configurations for breast cancer detection using microwave imaging," Progress In Electromagnetics Research, Vol. 99, 1-19, 2009.
doi:10.2528/PIER09100204

10. Nguyen, M. and R. Rangayyan, "Shape analysis of breast masses in mammograms via the fractial dimension," 27th Annual Conference of the IEEE Engineering in Medicine and Biology, 3210-3213, 2005.
doi:10.1109/IEMBS.2005.1617159

11. Chen, Y., I. Craddock, and P. Kosmas, "Feasibility study of lesion classification via contrast-agent-aided uwb breast imaging," IEEE Transactions on Biomedical Engineering, Vol. 57, No. 5, 1003-1007, 2010.
doi:10.1109/TBME.2009.2038788

12. Chen, Y., I. Craddock, P. Kosmas, M. Ghavami, and P. Rapajic, "Application of the mimo radar technique for lesion classification in UWB breast cancer detection ," 17th European Signal Processing Conference (EUSIPCO), 759-763, 2009.

13. Multiple-input multiple-output radar for lesion classification in ultrawideband breast imaging "IEEE Journal of Selected Topics in Signal Processing,", Vol. 4, No. 1, 187-201, 2010.

14. Conceicao, R. C., D. Byrne, M. O'Halloran, E. Jones, and M. Glavin, "Investigation of classifiers for early-stage breast cancer based on radar target signatures," Progress In Electromagnetics Research, Vol. 105, 295-311, 2010.
doi:10.2528/PIER10051904

15. Conceicao, R. C., M. O'Halloran, M. Glavin, and E. Jones, "Support vector machines for the classification of early-stage breast cancer based on radar target signatures ," Progress In Electromagnetics Research B, Vol. 23, 311-327, 2010.
doi:10.2528/PIERB10062407

16. Davis, S. K., B. D. V. Veen, S. C. Hagness, and F. Kelcz, "Breast tumor characterization based on ultrawideband backscatter," IEEE Trans. Biomed. Eng., Vol. 55, No. 1, 237-246, 2008.
doi:10.1109/TBME.2007.900564

17. Muinonen, K., "Introducing the gaussian shape hypothesis for asteroids and comets ," Astronomy and Astrophysics, Vol. 332, 1087-1098, 1998.

18. Muinonen, K., "Light Scattering by Stochastically Shaped Particles," Chapter 11, Academic Press, 2000.

19. Lazebnik, M., L. McCartney, D. Popovic, C. B. Watkins, M. J. Lindstrom, J. Harter, S. Sewall, A. Magliocco, J. H. Booske, M. Okoniewski, and S. C., "A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries ," Phys. Med. Biol., Vol. 52, 2637-2656, 2007.
doi:10.1088/0031-9155/52/10/001

20. Lazebnik, M., D. Popovic, L. McCartney, C. B. Watkins, M. J. Lindstrom, J. Harter, S. Sewall, T. Ogilvie, A. Magliocco, T. M. Breslin, W. Temple, D. Mew, J. H Booske, M. Okoniewski, and S. C. Hagness, "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries," Phys. Med. Biol., Vol. 52, 6093-6115, 2007.
doi:10.1088/0031-9155/52/20/002

21. Taflove, A. and S. C. Hagness, "Computational Electrodynamics: The Finite-difference Time-domain Method," Artech House, Publishers, June 2005.

22. Maass, W., "Computation with spiking neurons," The Handbook of Brain Theory and Neural Networks, 1080-1083, 2003.

23. Gerstner, W. and W. Kistler, "Spiking Neuron Models," Cambridge University Press, New York, 2002.

24. Maass, W., "Networks of spiking neurons: The third generation of neural network models," Neural Networks, Vol. 10, No. 9, 1659-1671, 1997.
doi:10.1016/S0893-6080(97)00011-7

25. Maass, W., "Computing with spiking neurons," Pulsed Neural Networks, MIT Press, 85, 1999.
doi:10.1016/S0893-6080(97)00011-7

26. Maguire, L., T. McGinnity, B. Glackin, A. Ghani, A. Belatreche, and J. Harkin, "Challenges for large-scale implementations of spiking neural networks on FPGAs," Neurocomputing, Vol. 71, No. 1-3, 13-29, 2007.
doi:10.1016/j.neucom.2006.11.029

27. Floreano, D., N. Schoeni, G. Caprari, and J. Blynel, "Evolutionary bits'n'spikes," Artificial Life Eight, 335, 2003.

28. Morgan, F., S. Cawley, B. McGinley, S. Pande, L. McDaid, B. Glackin, J. Maher, and J. Harkin, "Exploring the evolution of NoC-based spiking neural networks on FPGAs," IEEE International Conference on Field-programmable Technology, 2009 FPT , 300-303, 2010.

29. Holland, J., "Adaptation in Natural and Artificial Systems," MIT Press, Cambridge, MA, 1992.

30. Yao, X., "Evolving artificial neural networks," Proceedings of the IEEE, Vol. 87, No. 9, 1423-1447, 1999.
doi:10.1109/5.784219

31. Hagras, H., A. Pounds-Cornish, M. Colley, V. Callaghan, and G. Clarke, "Evolving spiking neural network controllers for autonomous robots," IEEE International Conference on Robotics and Automation, Vol. 5, 4620-4626, 2004.

32. Floreano, D., N. Schoeni, G. Caprari, and J. Blynel, "Evolutionary bits'n'spikes," Proceedings of the Eighth International Conference on Artificial Life , 335-344, 2003.

33. Belatreche, A., L. P. Maguire, M. McGinnity, and Q. X. Wu, "Evolutionary design of spiking neural networks," New Mathematics and Natural Computation (NMNC), Vol. 2, No. 3, 237-253, 2006.
doi:10.1142/S179300570600049X

34. McGinley, B., M. O'Halloran, R. C. Conceicao, F. Morgan, M. Glavin, and E. Jones, "Spiking neural networks for breast cancer classification using radar target signatures," Progress In Electromagnetics Research C, Vol. 17, 79-94, 2010.
doi:10.2528/PIERC10100202

35. Rocke, P., B. McGinley, J. Maher, F. Morgan, and J. Harkin, "Investigating the suitability of FPAAs for evolved hardware spiking neural networks ," Proceedings of Evolvable Systems: From Biology to Hardware, 118-126, 2008.
doi:10.1007/978-3-540-85857-7_11

36. Kasabov, N., Evolving Connectionist Systems: The Knowledge Engineering Approach, Springer-Verlag Inc., New York, 2007.

37. Kasabov, N., "Integrative connectionist learning systems inspired by nature: Current models, future trends and challenges," Natural Computing, Vol. 8, No. 2, 199-218, 2009.
doi:10.1007/s11047-008-9066-z

38. Schliebs, S., M. Defoin-Platel, S. Worner, and N. Kasabov, "Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models ," Neural Networks, Vol. 22, No. 5-6, 623-632, 2009.
doi:10.1016/j.neunet.2009.06.038

39. Pande, S., F. Morgan, S. Cawley, B. McGinley, S. Carrillo, L. McDaid, and J. Harkin, "Embrace-sysc for analysis of noc-based spiking neural network architecture," IEEE System on a Chip Symposium (SOC), 2010.

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