Several studies have investigated 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 Evolved-Topology Spiking Neural Neural (SNN) presented here extends the use of evolutionary algorithms to determine an optimal number of neurons and interneuron connections, forming a robust and accurate Ultra Wideband Radar (UWB) breast cancer classifier. The classifier is examined using dielectrically realistic numerical breast models, and the performance of the classifier is compared to an existing Fixed-Topology SNN cancer classifier.
2. Nass, S. L., I. C. Henderson, J. C. Lashof and Beyond: Developing Technologies for the Early Detection of Breast Cancer, Mammography, National Academy Press, 2001.
3. Chaudhary, S. S., R. K. Mishra, A. Swarup, and J. M. Thomas, "Dielectric properties of normal and malignant human breast tissue at radiowave and microwave frequencies," Indian J. Biochem. Biophys, Vol. 21, 76-79, 1984.
4. Surowiec, A. J., S. S. Stuchly, J. R. Barr, and A. Swarup, "Dielectric properties of breast carcinoma and the surrounding tissues," IEEE Trans. Biomed. Eng., Vol. 35, No. 4, 257-263, 1988.
5. Joines, W. T., Y. Zhang, C. Li, and R. L. Jirtle, "The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz," Med. Phys., Vol. 21, No. 4, 547-550, 1994.
6. Campbell, A. M. and D. V. Land, "Dielectric properties of female human breast tissue measured in vitro at 3.2 GHz," Phys. Med. Biol., Vol. 37, No. 1, 193-210, 1992.
7. Lazebnik, M., L. McCartney, D. Popovic, C. B. Watkins, M. J. Lindstrom, J. Harter, S. Sewall, A. Magliocco, J. H. Booske, and M. Okoniewski, "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.
8. Lazebnik , M., D. Popovic, L. McCartney, C. B. Watkins, M. J. Lindstrom, J. Harter, S. Sewall, T. Ogilvie, A. Magliocco, and T. M. Breslin, "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignantv breast tissues obtained from cancer surgeries," Phys. Med. Biol., Vol. 52, 6093-6115, 2007.
9. Nguyen, M. and R. Rangayyan, "Shape analysis of breast masses in mammograms via the fractal dimension," IEEE Engineering in Medicine and Biology 27th Annual Conference, 3210-3213, 2005.
10. AlShehri, S. A., S. Khatun, A. B. Jantan, R. S. A. Raja Abdullah, R. Mahmood, and Z. Awang, "3D experimental detection and discrimination of malignant and benign breast tumor using nn-based UWB imaging system," Progress In Electromagnetics Research, Vol. 116, 221-237, 2011.
11. Conceicao, R. C., M. O'Halloran, M. Glavin, and E. Jones, "Effects of dielectric heterogeneity in the performance of breast tumour classifiers," Progress In Electromagnetics Research M, Vol. 17, 73-86, 2011.
12. Conceicao, R. C., M. O'Halloran, M. Glavin, and E. Jones, "Evaluation of features and classifiers for classification of early-stage breast cancer," Journal of Electromagnetic Waves and Applications, Vol. 25, No. 1, 1-14, 2011.
13. Conceicao, , R. C., 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.
14. 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.
15. 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.
16. Maass, W., "Networks of spiking neurons: The third generation of neural network models," Neural Networks, Vol. 10, No. 9, 1659-1671, 1997.
17. Maass, W. and "Computing with spiking neurons", Pulsed Neural Networks, 85, MIT Press, 1999.
18. Holland, J., Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1992.
19. Stanley, K. O. and R. Miikkulainen, "Evolving neural networks through augmenting topologies," Evolutionary Computation, Vol. 10, No. 2, 99-127, 2002.
20. Goldberg, D. and J. Richardson, "Genetic algorithms with sharing for multimodal function optimization," Proceedings of the Second International Conference on Genetic Algorithms and Their Application, 41-49, 1987.
21. O'Halloran, , M., B. McGinley, R. C. Conceicao, F. Morgan, E. Jones, and M. Glavin, "Spiking neural networks for breast cancer classi¯cation in a dielectrically heterogeneous breast ," Progress In Electromagnetics Research, Vol. 113, 413-428, 2011.
22. Pande, , S., F. Morgan, C. Seamus, B. Mc Ginley, 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.
23. 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.
24. Muinonen, K., Introducing the gaussian shape hypothesis for asteroids and comets, "Astronomy and Astrophysics,", Vol. 332, 1087-1098, 1998.
25. Mishchenko, M. I., "Light scattering by stochastically shaped particles," Light Scattering by Nonspherical Particles: Theory, Measurements, and Applications, Ch. 11, Academic Press, 2000.