Systematic Optimization of Training and Setting of SVM-Based Microwave Stroke Classification: Numerical Simulations for 10 Port System

Abstract

The primary objective of this study is to systematically evaluate the performance of the Support Vector Machine (SVM) algorithm, identifying optimal configurations and appropriate parameters for training and testing data, for microwave brain stroke classification. Using experimentally verified 3D numerical models, a large database of synthetic training and test data has been created with different levels of data variability. These models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head models Within these models, strokes of varying sizes, types, and dielectric parameters are virtually inserted at different positions in brain within the plane of the antennas. Synthetic data sets have been generated to study the impact of reducing training data, data dimensionality, data format, and algorithm settings. The results of this study confirm that Principal Component Analysis (PCA) dimensionality reduction significantly improved the classification accuracy of the SVM algorithm, and datasets of subjects with smaller strokes appeared to be the most suitable for training. Furthermore, datasets that contain the real and imaginary parts of transmission and reflection coefficients result in the highest classification accuracy. For the current antenna array, the best observed setting and scenarios with high variability in training and test data, close to real clinical scenarios, the ability to accurately classify ischemic strokes and suggest safe initiation of thrombotic therapy is approximately 70%.