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2009-04-14

UWB Imaging for Breastr Cancer Detection Using Neural Network

By Saleh Ali AlShehri and Sabira Khatun
Progress In Electromagnetics Research C, Vol. 7, 79-93, 2009
doi:10.2528/PIERC09031202

Abstract

This paper presents a simple feed-forward back-propagation Neural Network (NN) model to detect and locate early breast cancer/tumor efficiently through the investigation of Electromagnetic (EM) waves. A spherical tumor of radius 0.25 cm was created and placed at arbitrary locations in a breast model using an EM simulator. Directional antennas were used to transmit and receive Ultra-Wide Band (UWB) signals in 4 to 8 GHz frequency range. Small training and validation sets were constructed to train and test the NN. The received signals were fed into the trained NN model to find the presence and location of tumor. Very optimistic results (about 100% and 94.4% presence and location detection rate of tumor respectively) have been observed for early received signal components with the NN model. Hence, the proposed model is very potential for early tumor detection to save human lives in the future.

Citation


Saleh Ali AlShehri and Sabira Khatun, "UWB Imaging for Breastr Cancer Detection Using Neural Network," Progress In Electromagnetics Research C, Vol. 7, 79-93, 2009.
doi:10.2528/PIERC09031202
http://www.jpier.org/PIERC/pier.php?paper=09031202

References


    1. Shao, W. and B. Zhou, "UWB microwave imaging for breast tumor detection in inhomogeneous tissue," Proceedings of the 2005 IEEE Engineering in Medicine and Biology, 27th Annual Conference, 1496-1499, Shanghai, China, 2005.
    doi:10.1109/IEMBS.2005.1616715

    2. Sill, J. M. and E. C. Fear, "Tissue sensing adaptive radar for breast cancer detection-experimental investigation of simple tumor models," IEEE Transactions on Microwave Theory and Techniques, Vol. 53, 3312-3319, 2005.
    doi:10.1109/TMTT.2005.857330

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

    4. Huo, Y., R. Bansal, and Q. Zhu, "Breast tumor characterization via complex natural resonances," IEEE MTT-S International Microwave Symposium Digest, Vol. 18, 387-390, 2003.

    5. Paulsen, K. D. and P. M. Meaney, "Nonactive antenna compensation for fixed-array microwave imaging — Part I: Model development," IEEE Transactions on Medical Imaging, Vol. 18, 496-507, 1999.
    doi:10.1109/42.781015

    6. Meaney, P. M., K. D. Paulsen, J. T. Chang, M. W. Fanning, and A. Hartov, "Nonactive antenna compensation for fixed-array microwave imaging — Part II: Imaging results," IEEE Transactions on Medical Imaging, Vol. 18, 508-518, 1999.
    doi:10.1109/42.781016

    7. Fear, E. C., S. C. Hagness, P. M. Meaney, M. Okoniewski, and M. A. Stuchly, "Enhancing breast tumor detection with near-field imaging," IEEE Microwave Magazine, Vol. 3, 48-56, 2002.
    doi:10.1109/6668.990683

    8. Bond, E. J., X. Li, S. C. Hagness, and B. D. Van Veen, "Microwave imaging via space-time beam forming for early detection of breast cancer," IEEE Trans. Antennas Propag., Vol. 51, 1690-1705, 2003.
    doi:10.1109/TAP.2003.815446

    9. Li, X., S. K. Davis, S. C. Hagness, D. W. Weide, and B. D. Veen, "Microwave imaging via space-time beam forming: Experimental investigation of tumor detection in multilayer breast phantoms," IEEE Trans. Microwav. Theory Tech., Vol. 52, 1856-1865, 2004.
    doi:10.1109/TMTT.2004.832686

    10. Shannon, C., E. Fear, and M. Okoniewski, "Dielectric-filled slotline bowtie antenna for breast cancer detection," Electronics Letters, Vol. 41, 388-390, 2005.
    doi:10.1049/el:20057336

    11. Sha, L., E. R. Ward, and B. Story, "A review of dielectric properties of normal and malignant breast tissue," Proceedings IEEE SoutheastCon, 457-462, 2002.

    12. Abbosh, A. M. and M. E. Bialkowski, "Design of UWB planar for microwave imaging systems," IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 24-27, Dubai, United Arab Emirates, 2007.

    13. Fear, E. C. and M. A. Stuchly, "Microwave detection of breast cancer," IEEE Transactions on Microwave Theory and Techniques, Vol. 48, 1854-1863, 2000.
    doi:10.1109/22.883862

    14. Miyakawa, M., T. Ishida, and M. Wantanabe, "Imaging capability of an early stage breast tumor by CP-MCT," Proceedings of the 26th Annual International Conference of the IEEE EMBS, Vol. 1, 1427-1430, San Francisco, CA, USA, 2004.

    15. Wang, M., S. Yang, S. Wu, and F. Luo, "A RBFNN approach for DoA estimation of ultra wideband antenna array," Neurocomputing, Vol. 71, 631-640, 2008.
    doi:10.1016/j.neucom.2007.08.023

    16. Klemm, M., I. Craddock, J. Leendertz, A. Preece, and R. Benjamin, "Experimental and clinical results of breast cancer detection using UWB microwave radar," Proceedings of IEEE Antennas and Propagation Society International Symposium, 1-4, 2008.

    17. Lim, H. B., N. T. Nhung, E. Li, and N. D. Thang, "Confocal microwave imaging for breast cancer detection: Delay-multiplyand- sum image reconstruction algorithm," IEEE Transaction on Biomedical Engineering, Vol. 55, 1697-1704, 2008.
    doi:10.1109/TBME.2008.919723

    18. Bindu, G., A. Lonappan, V. Thomas, C. K. Ananadan, and K. T. Mathew, "Active microwave imaging for breast cancer detection," Progress In Electromagnetic Research, Vol. 58, 149-169, 2006.
    doi:10.2528/PIER05081802

    19. Fear, E. C., J. Still, and M. A. Stuchly, "Experimental feasibility study of confocal microwave imaging for breast tumor detection," IEEE Transactions on Microwave Theory and Techniques, Vol. 51, 887-897, 2003.
    doi:10.1109/TMTT.2003.808630

    20. Davis, S. K., H. Tandradinata, S. C. Hagness, and B. D. Veen, "Ultrawideband microwave breast cancer detection: A detection-theoretic approach using the generalized likelihood ratio test ," IEEE Transactions on Biomedical Engineering, Vol. 52, 1237-1250, 2005.
    doi:10.1109/TBME.2005.847528

    21. Lazebnik, M., et al., "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries," IOP PUBLISHING, Phys. Med. Biol., Vol. 52, 6093-6115, 2007.

    22. CST MICROWAVE STUDIO, CST inc., 2008.

    23. Bishop, C. M., Neural Networks for Pattern Recognition, Oxford University Press, 1997.

    24. Zhang, Y. and L.Wu, "Weights optimization of neural network via improved BCO approach," Progress In Electromagnetic Research, Vol. 83, 185-198, 2008.
    doi:10.2528/PIER08051403

    25. Shiva Nagendra, S. M. and Mukesh Khare, "Artificial neural network approach for modeling nitrogen dioxide dispersion from vehicular exhaust emissions," Elsevier, Ecological Modeling, Vol. 190, 99-115, 2006.
    doi:10.1016/j.ecolmodel.2005.01.062

    26. Messer, K. and J. Kittler, "Choosing an optimal neural network size to aid a search through a large image database," Proceedings of the Ninth British Machine Vision Conference, BMVC 98, 1998.

    27. Wang, L. and H. Quek, "Optimal size of a feedforward neural network: How much does it matter?," IEEE, Proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services (ICAS/ICNS 2005), 2005.