On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification

Abstract

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

Classification and Location of Cerebral Hemorrhage Points Based on SEM and SSA-GA-BP Neural Network

“…In this paper, the raw signals were set as the input data of the Learning-by-Examples (LBE) strategies. The study in [19] is a continuation of the previous research in [20]. In [19], the raw signals were proposed by using the Principal Component Analysis (PCA) algorithm to improve the accuracy of the classification.…”

“…The study in [19] is a continuation of the previous research in [20]. In [19], the raw signals were proposed by using the Principal Component Analysis (PCA) algorithm to improve the accuracy of the classification. However, the algorithms proposed in [20] only trained on the data obtained from numerical simulations.…”

“…The group of King's College London, Strand, London, applied the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm to differentiate the hemorrhagic and S ischemic strokes [16], [17], [18]. In the latest study, the team from Czech Technical University used support vector machines (SVMs) to establish a comprehensive training set, which can classify hemorrhagic stroke and ischemic stroke [19]. The head phantom used in this reference only contains one material with dielectric properties equal to the average dielectric parameters of a human head.…”

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

“…SVMs were selected for this work for their reportedly high performance in stroke classification [24,78]. Moreover, SVMs are guaranteed to converge to the global minimum regardless of initial conditions due to their implementation of quadratic programming [79].…”

EnDairy: An Ensemble Learning-based Framework for Efficient Dairy Product Identification

“…Support vector machine (SVM) is commonly used to solve binary classification problems and is widely used in areas such as image processing, text recognition, and medical detection (Botros, Mourad-Chehade, & Laplanche, 2022;Pokorny et al, 2023). It has recently been applied to detecting several adulterants in milk with remarkable results (Amsaraj, Ambade, & Mutturi, 2021).In this experiment, we use the support vector machine (SVM) as the second baseline model with penalty factors set to: 0:1,1:3.6 and gamma of 0.1.…”