Microwave Spectroscopy Based Classification of Rat Hepatic Tissues: On the Significance of Dataset

Year 2020, , 307 - 313, 30.10.2020

Tuba Yilmaz

https://doi.org/10.17694/bajece.775198

Abstract

With the advancements in machine learning (ML) algorithms, microwave dielectric spectroscopy emerged as a potential new technology for biological tissue and material categorization. Recent studies reported the successful utilization of dielectric properties and Cole-Cole parameters. However, the role of the dataset was not investigated. Particularly, both dielectric properties and Cole-Cole parameters are derived from the S parameter response. This work investigates the possibility of using S parameters as a dataset to categorize the rat hepatic tissues into cirrhosis, malignant, and healthy categories. Using S parameters can potentially remove the need to derive the dielectric properties and enable the utilization of microwave structures such as narrow or wideband antennas or resonators. To this end, in vivo dielectric properties and S parameters collected from hepatic tissues were classified using logistic regression (LR) and adaptive boosting (AdaBoost) algorithms. Cole-Cole parameters and a reproduced dielectric property data set were also investigated. Data preprocessing is performed by using standardization and principal component analysis (PCA). Using the AdaBoost algorithm over 93% and 88% accuracy is obtained for dielectric properties and S parameters, respectively. These results indicate that the classification can be performed with a 5% accuracy decrease indicating that S parameters can be an alternative dataset for tissue classification.

Keywords

Cole-Cole parameters, dielectric properties, in vivo measurements, machine learning, rat hepatic tissues

Supporting Institution

Avrupa Birligi ve Istanbul Teknik Universitesi

Project Number

750346, 41554

Thanks

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 750346 and the Istanbul Technical University under grant agreement 41554.

References

• T. U. Gürbüz, B. Aslanyürek, A. Yapar, H. Şahintürk, I. Akduman. "A Nonlinear Microwave Breast Cancer Imaging Approach Through Realistic Body–Breast Modeling." IEEE Transactions on Antennas and Propagation, vol. 62. 5, 2014, pp. 2596-2605.
• M. Converse, E. J. Bond, S. C. Hagness, B. D. Van Veen. "Ultrawide-band microwave space-time beamforming for hyperthermia treatment of breast cancer: a computational feasibility study." IEEE Transactions on Microwave Theory and Techniques, vol. 52. 8, 2004, pp. 1876-1889.
• T. Yilmaz, R. Foster, Y. Hao. "Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels. " Diagnostics, vol 9.1, 2019, pp. 1-34.
• D. Popovic, L. McCartney, C. Beasley, M. Lazebnik, M. Okoniewski, S. C. Hagness, J. H. Booske."Precision open-ended coaxial probes for in vivo and ex vivo dielectric spectroscopy of biological tissues at microwave frequencies." IEEE Transactions on Microwave Theory and Techniques, vol. 53.5, 2005, pp. 1713-1722.
• Keysight Technologies. Probe Characteristics and Specifications, Keysight N1501A, Dielectric Probe Kit 10 MHz to 50 GHz. Available online:https://literature.cdn.keysight.com/litweb/pdf/5992-0264EN.pdf? id=2605692 (accessed on 25 July 2020).
• B. Saçlı, C. Aydınalp, G. Cansız, S. Joof, T. Yilmaz, M. Çayören, B. Önal, I. Akduman. "Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm." Computers in biology and medicine, vol. 112. 2019, pp. 103366.
• T. Yilmaz. "Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues. " Sensors, vol. 20, 2020, pp. 530.
• T. Yilmaz, M. A. Kılıç, M. Erdoğan, M. Çayören, D. Tunaoğlu, İ. Kurtoğlu, Y. Yaslan et al. "Machine learning aided diagnosis of hepatic malignancies through in vivo dielectric measurements with microwaves." Physics in medicine & biology, vol 61.13, 2016, pp. 5089.
• S. Gabriel, R. W. Lau, C. Gabriel. "The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues." Physics in medicine & biology, vol. 41.11, 1996, pp. 2271.
• T. Yilmaz, F. Ates Alkan. “In Vivo Dielectric Properties of Healthy and Benign Rat Mammary Tissues from 500 MHz to 18 GHz.” Sensor, vol. 20, pp. 2214.
• T. Jolliffe, J. Cadima. "Principal component analysis: a review and recent developments." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, pp. 2065, 2016.
• L. Shen, E.C. Tan. "Dimension reduction-based penalized logistic regression for cancer classification using microarray data." IEEE/ACM Transactions on computational biology and bioinformatics, vol. 2.2, 2005, pp. 166-175.
• T. Hastie, R. Saharon, J. Zhu, H. Zou. "Multi-class adaboost." Statistics and its Interface, vol 2.3, 2009, pp. 349-360.
• Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, 2011, pp. 2825-2830.