The study done in this paper focuses on the detection of breast cancer by neuronal approach, by rotating the transmitting antenna from 15°, 30°, 45°, 60°, 75° to 90° relative to its initial position which is of 0° (i.e. to the opposite of the reciving antenna). We have generated our database by using a CST electromagnetic simulator for each antenna location. Then the learning and test phases of our artificial neural network (ANN) are done for seven antennae locations using two learning algorithms which are: the Scaled Conjugate Gradient Back-propagation (Trainscg) and the Gradient Descent with Momentum (Traingdm). A comparative study was conducted for all the seven cases. The results obtained are very satisfying and show that the best location of the transmitter antenna is at 60° and that the learning algorithm Trainscg gives better results than Traingdm.
Sidi Mohammed Meriah,
"A Comparative Study for Breast Cancer Detection by Neural Approach for Different Configurations of the Microwave Imaging System," Progress In Electromagnetics Research M,
Vol. 65, 69-78, 2018. doi:10.2528/PIERM17111903
1. Conceicao, R. C., M. O’Halloran, M. Glavin, and E. Jones, "Numerical modelling for ultra wideband radar breast cancer detection and classification," Progress In Electromagnetics Research B, Vol. 34, 145-171, 2011. doi:10.2528/PIERB11072705
2. Fear, E. C., S. C. Hagness, P. M. Meany, M. Okoniewski, and A. Stuchlym, "Enhancing breast tumor detection with near field imaging," IEEE Microwave Magazine, Vol. 3, 48-56, 2002. doi:10.1109/6668.990683
3. 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 mammography and clinical breast examinations," New England Journal of Medicine, Vol. 338, 1089-1096, 1998. doi:10.1056/NEJM199804163381601
4. Li, X. and S. C. Hagness, "A confocal microwave imaging algorithm for breast cancer detection," IEEE Microwave and Wireless Components Letters, Vol. 11, No. 3, March 2001.
5. Al Shehri, S. A. 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
6. Fear, E. C. and M. A. Stuchly, "Microwave detection of breast cancer," IEEE Transactions on Microwave Theory and Techniques, Vol. 48, 1854-1863, 2000.
7. Chaudhary, S. S., R. K. Mishra, A. Swarup, and J. M. Thomas, "Dielectric properties of normal and malignant human breast tissues at radiowave and microwave frequencies," Indian Journal of Biochemistry and Biophysics, Vol. 21, 76-79, 1981.
8. Alshehri, S. A., "Experimental breast tumor detection using NN-based UWB imaging," Progress In Electromagnetics Research, Vol. 111, 447-465, 2011. doi:10.2528/PIER10110102
9. Alshehri, S. A., "3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging," Progress In Electromagnetics Research, Vol. 116, 221-237, 2011. doi:10.2528/PIER11022601
10. O’Halloran, M., B. McGinley, R. C. Conceicao, F. Morgan, E. Jones, and M. 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
11. Furundzicn, D., M. Djordjevic, and A. J. Bekic, "Neural networks approach to early breast cancer detection," Journal of Systems Architecture, Vol. 44, No. 617, 6339, 1998.
12. Bindu, G., A. Lonappan, V. Thomas, C. K. Aanandan, and K. T. Mathew, "Active microwave imaging for breast cancer detection," Progress In Electromagnetics Research, Vol. 58, 149-169, 2006. doi:10.2528/PIER05081802
13. Seladji, N., F. Z. Marouf, L. Merad, S. M. Meriah, F. T. Bendimerad, M. Bousahla, and N. Benahmed, "Antenne microruban miniature ultra large bande ULB pour imagerie microonde," Proceedings of the Congrès Méditerranéen des Télécommunications (CMT’12), 21-25, Fès, Morocco, March 22–24, 2012.
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. Miraoui, A., L. Merad, and S. M. Meriah, "Breast tumor classification using support vector machine and artificial neural networks," International Journal of Microwave and Optical Technology, Vol. 12, No. 2, March 2017.
16. Miraoui, A., L. Merad, and S. M. Meriah, "Microwave imaging for the detection and localization of breast cancer using artificial neural network," Journal of Theoretical and Applied Information Technology, Vol. 74, No. 3, April 30, 2015.