volume | PIER Journals

Abstract

Object: The varying breast morphologies can lead to enormous differences in microwave backscatter signals, making it difficult to identify weak tumor responses, which adversely affects the performance of microwave detection. Existing deep learning methods for microwave tumor detection struggle to generalize across diverse breast morphologies. The purpose of this study is to develop a deep learning method to overcome the influence of breast morphology on microwave tumor detection. Methods: This paper proposes a domain-adversarial tumor detection network (DATDNet) to improve detection performance. The proposed method employs breast backscatter signals with known tumor information as source domain data for training a convolution neural network. Subsequently, deep adversarial training is conducted on the backscatter signals of breasts with unseen morphologies and unknown tumor information in the trained network, in order to mitigate the adverse effects of variations in breast morphology on detection. In the process of microwave breast image feature extraction, our method introduces channel and spatial attention mechanisms in the convolution modules to pay more attention to tumor information. Results: The feature distribution estimations demonstrate that the microwave data from different breast morphologies are effectively aligned. In two datasets with completely different breast morphologies, the detection accuracy reaches 76.64% and 83.15%, with an improvement of 5.36% and 7.79% compared with baseline CNN. The ablation studies demonstrate that the proposed method effectively enhances the generalization performance and accuracy of microwave breast cancer detection.

1. Siegel, Rebecca L., Kimberly D. Miller, Nikita Sandeep Wagle, and Ahmedin Jemal, "Cancer statistics, 2023," CA: A Cancer Journal for Clinicians, Vol. 73, No. 1, 17-48, 2023.

2. Lazebnik, Mariya, Dijana Popovic, Leah McCartney, Cynthia B. Watkins, Mary J. Lindstrom, Josephine Harter, Sarah Sewall, Travis Ogilvie, Anthony Magliocco, Tara M. Breslin, et al., "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries," Physics in Medicine & Biology, Vol. 52, No. 20, 6093-6115, 2007.

3. Sugitani, Takumi, Shin-Ichi Kubota, Shin-Ichiro Kuroki, Kenta Sogo, Koji Arihiro, Morihito Okada, Takayuki Kadoya, Michihiro Hide, Miyo Oda, and Takamaro Kikkawa, "Complex permittivities of breast tumor tissues obtained from cancer surgeries," Applied Physics Letters, Vol. 104, No. 25, 253702, 2014.

4. Lu, Min, Xia Xiao, Yanwei Pang, Guancong Liu, and Hong Lu, "Detection and localization of breast cancer using UWB microwave technology and CNN-LSTM framework," IEEE Transactions on Microwave Theory and Techniques, Vol. 70, No. 11, 5085-5094, 2022.

5. Song, Hang, Shinsuke Sasada, Takayuki Kadoya, Koji Arihiro, Morihito Okada, Xia Xiao, Tomomi Ishikawa, Declan O'Loughlin, Jun-Ichi Takada, and Takamaro Kikkawa, "Cross-correlation of confocal images for excised breast tissues of total mastectomy," IEEE Transactions on Biomedical Engineering, Vol. 71, No. 5, 1705-1716, 2024.

6. Wang, Jingjing, Mengmeng Zhang, Yuxi Bai, Huaqing Xu, and Yucheng Fan, "Distance compensation-based dual adaptive artifact removal algorithm in microwave breast tumor imaging system," Biomedical Signal Processing and Control, Vol. 88, 105598, 2024.

7. Karam, Seyyed Abbas Shah, Declan O'Loughlin, and Babak Mohammadzadeh Asl, "A novel sophisticated form of DMAS beamformer: Application to breast cancer detection," Biomedical Signal Processing and Control, Vol. 74, 103516, 2022.

8. Liu, Guancong, Xia Xiao, Hang Song, Min Lu, and Takamaro Kikkawa, "An adaptive window-based hybrid artifact removal method for ultra-wide band imaging enhancement of early breast cancer detection," Biomedical Signal Processing and Control, Vol. 70, 102980, 2021.

9. Al-Zuhairi, Dheyaa T., Ahmed M. Abed, John M. Gahl, and Naz E. Islam, "Phase-based window function and CD-DMAS beamforming for microwave breast cancer detection," IET Microwaves, Antennas & Propagation, Vol. 14, No. 7, 608-616, 2020.

10. Albaaj, Azhar, Yaser Norouzi, and Gholamreza Moradi, "Multivariate and multi-dimensional CFAR radar image for breast cancer detection," Signal, Image and Video Processing, Vol. 18, No. 1, 647-656, 2024.

11. Golnabi, Amir H., Paul M. Meaney, Shireen D. Geimer, and Keith D. Paulsen, "3-D microwave tomography using the soft prior regularization technique: Evaluation in anatomically realistic MRI-derived numerical breast phantoms," IEEE Transactions on Biomedical Engineering, Vol. 66, No. 9, 2566-2575, 2019.

12. Liu, Guangdong, "Microwave tomographic imaging of anatomically realistic numerical phantoms with debye dispersion for breast cancer detection using a regularized inverse scattering technique in the time domain," Chinese Journal of Electronics, Vol. 32, No. 5, 1133-1150, 2023.

13. Nakajima, Mutsuki and Shouhei Kidera, "A radar-tomographic bidirectional method for quantitative microwave breast imaging," IEEE Transactions on Antennas and Propagation, Vol. 71, No. 12, 9814-9829, 2023.

14. Sekkal, Wassila, Lotfi Merad, and 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.

15. Halim, Ahmad Ashraf Abdul, Vijayasarveswari Veeraperumal, Allan Melvin Andrew, Mohd Najib Mohd Yasin, Mohd Zamri Zahir Ahmad, Kabir Hossain, Bifta Sama Bari, and Fatinnabila Kamal, "UWB-based early breast cancer existence prediction using artificial intelligence for large data set," Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol. 29, No. 2, 81-90, 2023.
doi:10.37934/araset.29.2.8190

16. Vijayasarveswari, V., A. M. Andrew, M. Jusoh, T. Sabapathy, R. A. A. Raof, M. N. M. Yasin, R.B. Ahmad, S. Khatun, and H. A. Rahim, "Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction," PloS One, Vol. 15, No. 8, e0229367, 2020.

17. Edwards, Keeley, Joe LoVetri, Colin Gilmore, and Ian Jeffrey, "Machine-learning-enabled recovery of prior information from experimental breast microwave imaging data," Progress In Electromagnetics Research, Vol. 175, 1-11, 2022.
doi:10.2528/PIER22051601

18. Martins, Raquel A., João M. Felício, Jorge R. Costa, and Carlos A. Fernandes, "Comparison of slot-based and Vivaldi antennas for breast tumor detection using machine learning and microwave imaging algorithms," 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-5, Dusseldorf, Germany, Mar. 2021.

19. Santorelli, Adam, Yunpeng Li, Emily Porter, Milica Popović, and Mark Coates, "Investigation of classification algorithms for a prototype microwave breast cancer monitor," The 8th European Conference on Antennas and Propagation (EuCAP 2014), 320-324, The Hague, Netherlands, Apr. 2014.

20. Song, Hongchao, Yunpeng Li, and Aidong Men, "Microwave breast cancer detection using time-frequency representations," Medical & Biological Engineering & Computing, Vol. 56, No. 4, 571-582, 2018.

21. Franceschini, Stefano, Maria Maddalena Autorino, Michele Ambrosanio, Vito Pascazio, and Fabio Baselice, "A deep learning approach for diagnosis support in breast cancer microwave tomography," Diagnostics, Vol. 13, No. 10, 1693, 2023.

22. Rana, Soumya Prakash, Maitreyee Dey, Riccardo Loretoni, Michele Duranti, Mohammad Ghavami, Sandra Dudley, and Gianluigi Tiberi, "Radiation-free microwave technology for breast lesion detection using supervised machine learning model," Tomography, Vol. 9, No. 1, 105-129, 2023.

23. Lu, Min, Xia Xiao, Guancong Liu, and Hong Lu, "Microwave breast tumor localization using wavelet feature extraction and genetic algorithm-neural network," Medical Physics, Vol. 48, No. 10, 6080-6093, 2021.

24. Reimer, Tyson and Stephen Pistorius, "The diagnostic performance of machine learning in breast microwave sensing on an experimental dataset," IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, Vol. 6, No. 1, 139-145, 2022.

25. Lu, Min, Xia Xiao, Guancong Liu, Hong Lu, Yanwei Pang, and Takamaro Kikkawa, "Breast tumor detection by 1D-convolutional neural network based on ultra-wide-band microwave technology," Measurement Science and Technology, Vol. 34, No. 2, 025702, 2022.

26. Khalid, Nazish, Muhammad Hashir, Nasir Mahmood, Muhammad Asad, Muhammad A. Rehman, Muhammad Q. Mehmood, Muhammad Zubair, and Yehia Massoud, "Efficient deep learning approaches for automated tumor detection, classification, and localization in experimental microwave breast imaging data," 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, Monterey, CA, USA, May 2023.

27. Conceição, Raquel C., Hugo Medeiros, Daniela M. Godinho, Martin O'Halloran, Diego Rodriguez-Herrera, Daniel Flores-Tapia, and Stephen Pistorius, "Classification of breast tumor models with a prototype microwave imaging system," Medical Physics, Vol. 47, No. 4, 1860-1870, 2020.

28. Gerazov, Branislav and Raquel C. Conceicao, "Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging," IEEE EUROCON 2017 --- 17th International Conference on Smart Technologies, 564-569, Ohrid, Macedonia, 2017.

29. Janjic, Aleksandar, Ibrahim Akduman, Mehmet Cayoren, Onur Bugdayci, and Mustafa Erkin Aribal, "Microwave breast lesion classification-results from clinical investigation of the safe microwave breast cancer system," Academic Radiology, Vol. 30, S1-S8, 2023.

30. Lazebnik, Mariya, Michal Okoniewski, John H. Booske, and Susan C. Hagness, "Highly accurate Debye models for normal and malignant breast tissue dielectric properties at microwave frequencies," IEEE Microwave and Wireless Components Letters, Vol. 17, No. 12, 822-824, 2007.

31. O'Halloran, Martin, Raquel Cruz Conceicao, Dallan Byrne, Martin Glavin, and Edward Jones, "FDTD modeling of the breast: A review," Progress In Electromagnetics Research B, Vol. 18, 1-24, 2009.

32. Shao, Wenyi, Arezou Edalati, Todd R. McCollough, and William J. McCollough, "A phase confocal method for near-field microwave imaging," IEEE Transactions on Microwave Theory and Techniques, Vol. 65, No. 7, 2508-2515, 2017.

33. Reimer, Tyson, Jordan Krenkevich, and Stephen Pistorius, "An open-access experimental dataset for breast microwave imaging," 2020 14th European Conference on Antennas and Propagation (EuCAP), 1-5, Copenhagen, Denmark, Mar. 2020.

34. Reimer, Tyson and Stephen Pistorius, "Review and analysis of tumour detection and image quality analysis in experimental breast microwave sensing," Sensors, Vol. 23, No. 11, 5123, 2023.
doi:10.3390/s23115123

35. Ganin, Yaroslav, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky, "Domain-adversarial training of neural networks," Journal of Machine Learning Research, Vol. 17, No. 59, 1-35, 2016.

36. Du, Yanan, Wanmin Liu, Yixuan Wang, Rutian Li, and Li Xie, "YOLO-CPC: A breast tumor detection and identification algorithm based on improved YOLOv7," Signal, Image and Video Processing, Vol. 19, No. 3, 260, 2025.

37. Woo, Sanghyun, Jongchan Park, Joon-Young Lee, and In So Kweon, "CBAM: Convolutional block attention module," Proceedings of the European Conference on Computer Vision (ECCV), 3-19, Munich, Germany, Sep. 2018.

38. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, "Attention is all you need," 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, Dec. 2017.

39. Solis-Nepote, Mario, Tyson Reimer, and Stephen Pistorius, "An air-operated bistatic system for breast microwave radar imaging: Pre-clinical validation," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1859-1862, Berlin, Germany, Jul. 2019.

Dr. Min Lu

Dr. Xia Xiao

Dr. Lei Yuan

Dr. Han Su

Dr. Xiaomin He

Dr. Yuan Yang