In this paper, an intracranial hemorrhage stroke detection and classification method using microwave imaging system (MIS) based on machine learning approaches is presented. To create a circular array-based MIS, sixteen elements of modified bowtie antennas around a multilayer head phantom with a spherical target with radius of 1 cm as an intracranial hemorrhage target are simulated in CST simulator. To obtain satisfied radiation characteristics in the desired frequency band of 0.5-5 GHz a suitable matching medium is designed. Initially, in the processing section, a confocal image-reconstructing method based on delay-and-sum (DAS) and delay-multiply-and-sum (DMAS) beam-forming algorithms is used. Then, reconstructed images are generated, which shows the applicability of the confocal method in detecting a spherical target in the range of 1 cm. Separating and categorizing targets is a challenging task due to the ambiguity in the extracted target from MIS. Thus, to distinguish between healthy and unhealthy brain tissues, a new compound machine learning technique, including filtering, edge-detection based segmentation, and applying K Means and fuzzy clustering techniques, which reveal intracranial hemorrhage area from reconstructed images is adopted. Simulated results are presented to validate the proposed method effectiveness for precisely localizing and classifying bleeding targets.
2. Sohani, B., G. Tiberi, N. Ghavami, M. Ghavami, S. Dudley, and A. Rahimi, "Microwave imaging for stroke detection: Validation on head-mimicking phantom," 2019 PhotonIcs & Electromagnetics Research Symposium --- Spring (PIERS --- SPRING), 940-948, Rome, Italy, Jun. 17-20, 2019.
3. Wang, J., X. Jiang, L. Peng, X. Li, H. An, and B. Wen, "Detection of neural activity of brain functional site based on microwave scattering principle," IEEE Access, Vol. 7, 13468-13475, 2019.
4. Ilja, M., A. Massa, D. Vrba, O. Fiser, M. Salucci, and J. Vrba, "Microwave tomography system for methodical testing of human brain stroke detection approaches," International Journal of Antennas and Propagation, 2019.
5. Santorelli, A., E. Porter, E. Kirshin, Y. J. Liu, and M. Popovic, "Investigation of classifiers for tumor detection with an experimental time domain breast screening system," Progress In Electromagnetics Research, Vol. 144, 45-57, 2014.
6. Pokorny, T. and J. Tesarik, "Microwave stroke detection and classification using different methods from MATLAB's classification learner toolbox," 2019 European Microwave Conference in Central Europe (EuMCE), 500-503, IEEE, 2019.
7. 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.
8. Rahama, Y. A., O. A. Aryani, U. A. Din, M. A. Awar, A. Zakaria, and N. Qaddoumi, "Novel microwave tomography system using a phased-array antenna," IEEE Trans. Microw. Theory Tech., Vol. 66, 5119-5128, 2018.
9. Franceschini, S., M. Ambrosanio, F. Baselice, and V. Pascazio, "Neural networks for inverse problems: The microwave imaging case," 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-5, IEEE, Mar. 2021.
10. Bevacqua, M. T., S. Di Meo, L. Crocco, T. Isernia, G. Matrone, and M. Pasian, "A quantitative approach for millimeter-wave breast cancer imaging," 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-3, IEEE, Mar. 2021.
11. Chaplot, S., L. M. Patnaik, and N. R. Jagannathan, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network," Biomedical Signal Processing and Control, Vol. 1, No. 1, 86-92, 2006.
12. Rana, S. P., M. Dey, G. Tiberi, L. Sani, A. Vispa, G. Raspa, M. Duranti, M. Ghavami, and S. Dudley, "Machine learning approaches for automated lesion detection in microwave breast imaging clinical data," Sci. Rep., Vol. 9, 1-12, 2019.
13. Dachena, C., A. Fedeli, A. Fanti, M. B. Lodi, M. Pastorino, and A. Randazzo, "A microwave imaging technique for neck diseases monitoring," 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-5, IEEE, Mar. 2021.
14. Ojaroudi, M., S. Bila, and M. Salimitorkamani, "A novel machine learning approach of hemorrhage stroke detection in differential microwave head imaging system," 2020 European Conference on Antennas and Propagation, 2020.
15. Nanni, L., S. Ghidoni, and S. Brahnam, "Handcrafted vs. non-handcrafted features for computer vision classification," Pattern Recognition, Vol. 71, 158-172, 2017, ISSN 0031-3203.
16. CST Microwave Studio. ver. 2016, CST, , Framingham, MA, USA, 2016.
17. Li, X., M. Jalilvand, Y. L. Sit, and T. Zwick, "A compact double-layer on-body matched bowtie antenna for medical diagnosis," IEEE Transactions on Antennas and Propagation, Vol. 62, No. 4, 1808-1816, 2014.
18. Jalilvand, M., X. Li, J. Kowalewski, and T. Zwick, "Broadband miniaturised bow-tie antenna for 3D microwave tomography," Electronics Letters, Vol. 50, No. 4, 244-246, 2014.
19. Jamlos, M. A., W. A. Mustafa, N. Husna, S. Z. Syed Idrus, W. Khairunizam, I. Zunaidi, Z. M. Razlan, and A. B. Shahriman, "Ultra-wideband confocal microwave imaging for brain tumor detection," IOP Conference Series: Materials Science and Engineering, Vol. 557, No. 1, 012002, IOP Publishing, 2019.
20. Chandra, R., H. Zhou, I. Balasingham, and R. M. Narayanan, "On the opportunities and challenges in microwave medical sensing and imaging," IEEE Transactions on Biomedical Engineering, Vol. 62, No. 7, 1667-1682, 2015.
21. Benny, R., T. A. Anjit, and P. Mythili, "An overview of microwave imaging for breast tumor detection," Progress In Electromagnetics Research B, Vol. 87, 61-91, 2020.
22. Ahsan, S., M. Koutsoupidou, E. Razzicchia, I. Sotiriou, and P. Kosmas, "Advances towards the development of a brain microwave imaging scanner," 2019 13th European Conference on Antennas and Propagation (EuCAP), 1-4, IEEE, Mar. 2019.
23. Fhager, A., S. Candefjord, M. Elam, and M. Persson, "3D simulations of intracerebral hemorrhage detection using broadband microwave technology," Sensors, Vol. 19, No. 16, 3482, 2019.
24. Wu, Y., B. Liu, and M. Zhu, "A single-pair antenna microwave medical detection system based on unsupervised feature learning," International Conference on Computational Social Networks, 404-414, Springer, Dec. 2018.
25. Zhang, Y.-D. and L. Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.
26. Parvati, K., P. Rao, and M. Mariya Das, "Image segmentation using gray-scale morphology and marker-controlled watershed transformation," Discrete Dynamics in Nature and Society, Vol. 2008, 2008.
27. Otsu, N., "A threshold selection method from gray-level histograms," IEEE Trans. Sys. Man. Cyber., Vol. 9, No. 1, 62-66, 1979, doi: 10.1109/TSMC.1979.4310076.
28. Liu, D., "Otsu method and K-means," Ninth International Conference on Hybrid Intelligent Systems, Vol. 1, 344-349, IEEE, 2009.
29. Vijayabhanu, R. and V. Radha, "Recognition and elimination of missing values and outliers from an anaerobic wastewater treatment system using K-Means cluster," 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Vol. 4, V4-186, 2010.
30. Tripathi, S., R. S. Anand, and E. Fernandez, "A review of brain MR image segmentation techniques," Proceedings of International Conference on Recent Innovations in Applied Science, Engineering & Technology, 16-17, 2018.
31. Guo, L. and A. Abbosh, "Stroke localization and classification using microwave tomography with k-means clustering and support vector machine," Bioelectromagnetics, Vol. 39, No. 4, 312-324, May 2018, doi: 10.1002/bem.22118.Epub2018Mar25. PMID: 29575011.