Microwave Imaging System of Breast Cancer Detection by Using Support Vector Machine Method

Main Article Content

Azhar Albaaj
Yaser Norouzi
Gholamreza Moradi

Keywords

Breast Cancer, Classification, Microwave imaging, Support Vector Machine

Abstract

Breast cancer is the most common disease among women in the world. Therefore, many doctors diagnose cancer in its early stages. Because early detection and treatment of the tumor help in faster recovery. The main objective of this paper is to build an application that is capable of “detecting and determining” a breast tumor using image processing in MATLAB. An open-source experimental database of the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID) was used. The most vital part is template design and the formation of the algorithm for breast tumor detection. Some of the key features used in this paper are - image acquisition, noise reduction, image resizing, tumor region identification, marking of tumors, etc. A Graphical User Interface (GUI) is also used to build a better interface for the user, it allows us to understand the algorithm's true potential and how it can be further developed due time. After an image is provided to the algorithm it processes the image and informs the user how harmful the tumor is to the patient, and what necessary steps should she take to find a cure for it. In this work, Support Vector Machines (SVM) were used to detect breast cancer using microwave imaging. We extract features from the data collected by microwave imaging and use SVM to classify them. This work can help to support microwave breast imaging as well as help to move away from false positives to obtain greater accuracy and detect tumors depicted as false negatives.

Abstract 209 | pdf Downloads 173

References

1. Monticciolo, D.L., et al., Breast cancer screening recommendations inclusive of all women at average risk: update from the ACR and Society of Breast Imaging. J Journal of the American College of Radiology, 2021. 18(9): p. 1280-1288.
2. Boyd, N.F., et al., Mammographic density and the risk and detection of breast cancer. J New England Journal of Medicine, 2007. 356(3): p. 227-236.
3. Singh, Rajveer Kumar. Sain, Mr. Nitesh, Etiology Of Breast Cancer. J Journal of Pharmaceutical Negative Results, 2023: p. 1427-1434.
4. Changizi, V. Giti, M. Kheradmand, A Arab, Application of computed aided detection in breast masses diagnosis. J Indian Journal of Cancer, 2008. 45(4): p. 164-166.
5. Seiffert, K., et al., The effect of family history on screening procedures and prognosis in breast cancer patients-Results of a large population-based case-control study. J The Breast, 2021. 55: p. 98-104.
6. Daniluk, K., et al., Use of selected carbon nanoparticles as melittin carriers for MCF-7 and MDA-MB-231 human breast cancer cells. J Materials, 2019. 13(1): p. 90.
7. Baines, Cornelia J. Mcfarlane, Douglas V. Miller, Anthony B, The role of the reference radiologist; estimates of inter-observer agreement and potential delay in cancer detection in the national breast screening study. J Investigative Radiology, 1990. 25(9): p. 971-976.
8. Chang, Y.-W., et al., A novel computer-aided-diagnosis system for breast ultrasound images based on BI-RADS categories. J Applied Sciences, 2020. 10(5): p. 1830.
9. Nguyen, V.D., et al. A program for locating possible breast masses on mammograms. in The Third International Conference on the Development of Biomedical Engineering in Vietnam: BME2010, 11–14 January 2010, Ho Chi Minh City, Vietnam. 2010. Springer.
10. Eadie, Leila H. Taylor, Paul. Gibson, Adam, A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. J European Journal of Radiology, 2012. 81(1): p. e70-e76.
11. Marias, K., et al., A registration framework for the comparison of mammogram sequences. J IEEE Transactions on Medical Imaging, 2005. 24(6): p. 782-790.
12. Berger, F., et al., Randomised, open-label, multicentric phase III trial to evaluate the safety and efficacy of palbociclib in combination with endocrine therapy, guided by ESR1 mutation monitoring in oestrogen receptor-positive, HER2-negative metastatic breast cancer patients: study design of PADA-1. 2022. 12(3): p. e055821.
13. Wassila, S., M. Lotfi, and M.S. Mohammed. Breast cancer Detection Using the SVR Approach For Different Configurations of Microwave Imaging System. in 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA). 2019. IEEE.
14. Kwon, Sollip. Lee, Seungjun, Recent advances in microwave imaging for breast cancer detection. 2016. 2016.
15. Albaaj, A., Norouzi, Y., and Moradi, G., Breast Cancer Detection Using Microwaves by Changing the Electrical Properties of Tissues. J Tobacco Regulatory Science, 2022: p. 2921-2931.
16. Mehdy, M., et al., Artificial neural networks in image processing for early detection of breast cancer. J Computational mathematical methods in medicine. 2017.
17. Cruz, R. and C. Martin, Numerical Modelling for Ultra Wideband Radar Breast Cancer Detection and Classification. PIER B, 2011. 34: p. 145-171.
18. Shalmani, A.N.R. Diagnosis of Breast Cancer Masses in Computer Aided Mammography Images. in Third International Conference on Recent Innovations in Electrical and Computer Engineering. 2016.
19. Tavakkolah P, S.R., A new approach to classifying and classifying breast cancer masses (Persian), in Third Information and Knowledge Technology Conference. 6-8 December 2007: Tehran; Iran.
20. Pezeshki, H., Rastgarpour, M., Sharifi, A., Yazdani, S., Extraction of spiculated parts of mammogram tumors to improve accuracy of classification. J Multimedia Tools Applications. 2019. 78: p. 19979-20003.
21. Mughal, B., Sharif, M., Muhammad, N., Saba, T., A novel classification scheme to decline the mortality rate among women due to breast tumor. J Microscopy research technique. 2018. 81(2): p. 171-180.
22. Byrne, D., O'Halloran, M., Jones, E., Glavin, M., Support vector machine-based ultrawideband breast cancer detection system. J Journal of Electromagnetic Waves Applications. 2011. 25(13): p. 1807-1816.
23. Reimer, T., M. Solis-Nepote, and S.J.D. Pistorius, The application of an iterative structure to the delay-and-sum and the delay-multiply-and-sum beamformers in breast microwave imaging. Diagnostics, 2020. 10(6): p. 411.
24. Reimer, T., J. Krenkevich, and S. Pistorius. An open-access experimental dataset for breast microwave imaging. In 2020 14th European Conference on Antennas and Propagation (EuCAP). 2020. IEEE.