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

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 a 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.