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.
Highlights
DIELECTRIC PROPERTY discrepancy between healthy and diseased biological tissues enabled many different applications of electromagnetics in medicine including but not limited to microwave hyperthermia, microwave breast cancer imaging, and microwave blood glucose detection [1,2,3]
Open-ended coaxial probes are commercially available for laboratory use; the technique has been widely utilized for dielectric property characterization of different materials including liquids, gel-like materials, along with biological tissues [4]
The measurement set-up included an open-ended coaxial probe connected to a Fieldfox N9923A Network Analyzer (NA) with an RF cable
Summary
DIELECTRIC PROPERTY discrepancy between healthy and diseased biological tissues enabled many different applications of electromagnetics in medicine including but not limited to microwave hyperthermia, microwave breast cancer imaging, and microwave blood glucose detection [1,2,3]. The characterization method is determined based on different features including the frequency of operation, nature of sample, and temperature range One such technique that has been used for broadband dielectric property. Previously reported studies proved that, without costly updates on the method, the accuracy of tissue classification can be increased by adopting machine learning (ML) based classification algorithms [6,7,8] Such algorithms are not necessarily concerned with determining the dielectric properties of the samples; they determine the type of the sample under test. The reported accuracy advancements were obtained by application of classification algorithms to two types of datasets the first and mostly used one is the dielectric property data and the second one is the parameters of mathematical models [6,7,8].
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