Deep Learning-based Classification of Breast Tumors using Raw Microwave Imaging Data

Year 2024, , 1327 - 1334, 25.09.2024

Mustafa Berkan Biçer , Uğur Eliiyi , Deniz Türsel Eliiyi

https://doi.org/10.2339/politeknik.1056839

Cited By: 2

Abstract

Breast cancer is the leading type of malignant neoplasm disease among women worldwide. Breast screening makes extensive use of powerful techniques such as x-ray mammography, magnetic resonance imaging, and ultrasonography. While these technologies have numerous benefits, certain drawbacks such as the use of low-energy ionizing x-rays, a lack of specificity for malignant tissues, and cost, have motivated researchers to investigate novel imaging and detection modalities. Microwave imaging (MWI) has been extensively studied due to its low-cost structure and ability to perform measurements using non-ionizing electromagnetic waves. This study proposes a novel convolutional neural network (CNN) model for detecting and classifying tumor scatterers in MWI simulation data. To accomplish this, 10001 different numerical breast models with tumor scatterers of varying numbers and positions were developed, and the simulation results were derived using the synthetic aperture radar (SAR) technique. The presented CNN structure was trained using 8000 pieces of simulation data, and the remaining data were used for testing, achieving accuracy rates of 99.61% and 99.75%, respectively. The proposed model is compared to three state-of-the-art models on the same dataset in terms of classification performance. The results demonstrate that the proposed model effectively performs effectively well in detecting and classifying tumor scatterers.

Keywords

breast cancer, classification, convolutional neural networks, deep learning, microwave imaging

Ham Mikrodalga Görüntüleme Verilerini Kullanarak Meme Tümörlerinin Derin Öğrenme Tabanlı Sınıflandırılması

Abstract

Meme kanseri, dünya genelinde kadınlar arasında en yaygın türdeki kötü huylu tümör hastalığıdır. Meme taraması, x-ışını mamografisi, manyetik rezonans görüntüleme ve ultrasonografi gibi güçlü teknikleri yoğun bir şekilde kullanır. Bu teknolojilerin birçok faydası olsa da, düşük enerjili iyonlaştırıcı x-ışınlarının kullanımı, kötü huylu dokular için yetersizlik ve maliyet gibi bazı dezavantajlar, araştırmacıları yeni görüntüleme ve tespit yöntemlerini araştırmaya teşvik etmiştir. Mikrodalga görüntüleme (MDG), düşük maliyetli yapısı ve iyonlaştırıcı olmayan elektromanyetik dalgalar kullanarak ölçümler yapabilme yeteneği nedeniyle yoğun bir şekilde araştırılmaktadır. Bu çalışma, MDG simülasyon verilerinde tümör saçaklarını tespit etmek ve sınıflandırmak için yeni bir ESA modeli önermektedir. Bunun için farklı sayı ve pozisyonlar tümör saçıcılarına sahip 10001 farklı sayısal meme modeli geliştirilmiştir ve sentetik açıklıklı radar (SAR) tekniği kullanılarak simülasyon sonuçları elde edilmiştir. Sunulan ESA yapısı, 8000 adet simülasyon verisi kullanılarak eğitilmiş ve kalan veriler test için kullanılarak sırasıyla %99.61 ve %99.75 doğruluk oranlarına ulaşılmıştır. Önerilen model, sınıflandırma performansı açısından aynı veri kümesi üzerinde üç farklı güncel modelle karşılaştırılmıştır. Sonuçlar, önerilen modelin tümör saçıcılarını tespit edip sınıflandırmada etkili bir şekilde çalıştığını göstermektedir.

Keywords

meme kanseri, sınıflandırma, evrişimsel sinir ağları, derin öğrenme, mikrodalga görüntüleme

References

• [1] Curado M. P., “Breast cancer in the world: incidence and mortality”, Salud Publica Mex., 53(5): 372–384, (2011).
• [2] Hassan A. M. and El-Shenawee M., “Review of electromagnetic techniques for breast cancer detection”, IEEE Rev. Biomed. Eng., 4: 103–118, (2011).
• [3] Nass S. J., Henderson I. C. and Lashof J. C., "Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer", National Academy Press, (2002).
• [4] Fear E. C., Meaney P. M. and Stuchly M. A., “Microwaves for breast cancer detection?”, IEEE Potentials, 22(1): 12, (2003).
• [5] Kurrant D., Sill J. and Fear E., “Tumor response estimation algorithm for radar-based microwave breast cancer”, Proceedings of the XXIXth URSI General Assembly, Chicago, 6–9, (2015).
• [6] Winters D. W., Shea J. D., Kosmas P., Van Veen B. D. and Hagness S. C., “Three-dimensional microwave breast imaging: Dispersive dielectric properties estimation using patient-specific basis functions”, IEEE Transactions on Medical Imaging, 28(7): 969–981, (2009).
• [7] Bindu G., Lonappan A., Thomas V., Aanandan C. K., Mathew K. T. and Abraham S. J., “Active microwave imaging for breast cancer detection”, Prog. Electromagn. Res., 58: 149–169, (2006).
• [8] Güren O., Çayören M., Ergene L. T. and Akduman I., “Surface impedance based microwave imaging method for breast cancer screening: Contrast-enhanced scenario”, Phys. Med. Biol., 59(19): 5725–5739, (2014).
• [9] Bicer M. B., Akdagli A. and Ozdemir C., “A matching-pursuit based approach for detecting and imaging breast cancer tumor”, Prog. Electromagn. Res. M, 64: 65–76, (2018).
• [10] M Bicer. B. and Akdagli A., “Implementation of the inverse circular radon transform-based imaging approach for breast cancer screening”, Int. J. RF Microw. Comput. Eng., 28(6): e21279, (2018).
• [11] Bicer M. B. and Akdagli A., “An experimental study on microwave imaging of breast cancer with the use of tumor phantom”, Appl. Comput. Electromagn. Soc. J., 32(10): 941–947, (2017).
• [12] Davis S. K., Van Veen B. D., Hagness S. C. and Kelcz F., “Breast tumor characterization based on ultrawideband microwave backscatter”, IEEE Trans. Biomed. Eng., 55(1): 237–246, (2008).
• [13] Chen Y. and Kosmas P., “Detection and localization of tissue malignancy using contrast-enhanced microwave imaging: Exploring information theoretic criteria”, IEEE Trans. Biomed. Eng., 59(3): 766–776, (2012).
• [14] Xie Y., Guo B., Xu L., Li J. and Stoica P., “Multistatic adaptive microwave imaging for early breast cancer detection”, IEEE Trans. Biomed. Eng., 53(8): 1647–1657, (2006).
• [15] Fear E. C., Li X., Hagness S. C. and Stuchly M. A., “Confocal microwave imaging for breast cancer detection: Localization of tumors in three dimensions”, IEEE Trans. Biomed. Eng., 49(8): 812–822, (2002).
• [16] Klemm M., Craddock I., Leendertz J., Preece A. and Benjamin R., “Experimental and clinical results of breast cancer detection using UWB microwave radar”, IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, 1–4, (2008).
• [17] N Irishina., Moscoso M., and Dorn O., “Microwave imaging for early breast cancer detection using a shape-based strategy”, IEEE Trans. Biomed. Eng., 56(4): 1143–1153, (2009).
• [18] Lim H. B., Nung N. T. T., Li E. P. and Thang N. D., “Confocal microwave imaging for breast cancer detection: Delay-multiply-and-sum image reconstruction algorithm”, IEEE Transactions on Biomedical Engineering, 55(6): 1697–1704, (2008).
• [19] Li X., Bond E. J., Van Veen B. D. and Hagness S. C., “An overview of ultra-wideband microwave imaging via space-time beamforming for early-stage breast-cancer detection”, IEEE Antennas Propag. Mag., 47(1): 19–34, (2005).
• [20] Nawel Z., Nabiha A., Nilanjan D. and Mokhtar S., “Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification”, J. Med. Imaging Heal. Informatics, 6(1): 53–62, (2016).
• [21] Tan M., Pu J. and Zheng B., “Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model”, Int. J. Comput. Assist. Radiol. Surg., 9(6): 1005–1020, (2014).
• [22] Al-Hadidi M. R. A., Al-Gawagzeh M. Y. and Alsaaidah B. A., “Solving mammography problems of breast cancer detection using: Artificial neural networks and image processing techniques”, Indian J. Sci. Technol., 5(4): 2520–2528, (2012).
• [23] Yin J., Yang J. and Jiang Z., “Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis”, Oncol. Lett., 17(3): 2623–2630, (2019).
• [24] Woitek R., Spick C., Schernthaner M., Rudas M., Kapetas P., Bernathova M., Furtner J., Pinker K., Helbich T. H. and Balzter P. A. T., “A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions”, Eur. Radiol., 27(9): 3799–3809, (2017).
• [25] Abdel-Nasser M., Melendez J., Moreno A., Omer O. A. and Puig D., “Breast tumor classification in ultrasound images using texture analysis and super-resolution methods”, Eng. Appl. Artif. Intell., 59: 84–92, (2017).
• [26] Klimonda Z., Karwat P., Dobruch-Sobczak K., Piotrzkowska-Wróblewska H. and Litniewski J., “Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue”, Sci. Rep., 9(1): 1–9, (2019).
• [27] Conceiçao R. C., O’Halloran M., Glavin M. and Jones E., “Support vector machines for the classification of early-stage breast cancer based on radar target signatures”, Prog. Electromagn. Res. B, 23: 311–327, (2010).
• [28] Chollet F., “Xception: Deep learning with depthwise separable convolutions”, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, (2017).
• [29] He K., Zhang X., Ren S. and Sun J., “Identity mappings in deep residual networks”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9908, (2016).
• [30] Huang G., Liu Z., Van Der Maaten L. and Weinberger K. Q., “Densely connected convolutional networks”, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, (2017).
• [31] Özdemir C., "Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms", Wiley-Interscience, (2012).
• [32] Deng J., Dong W., Socher R., Li L.-J., Li K. and Fei-Fei L., “ImageNet: A large-scale hierarchical image database”, (2010).