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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00375995" target="_blank" >RIV/68407700:21460/24:00375995 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/JERM.2024.3404119" target="_blank" >https://doi.org/10.1109/JERM.2024.3404119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JERM.2024.3404119" target="_blank" >10.1109/JERM.2024.3404119</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

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

Popis výsledku anglicky

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%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology

ISSN

2469-7249

e-ISSN

2469-7257

Svazek periodika

8

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

273-281

Kód UT WoS článku

001242894100001

EID výsledku v databázi Scopus

2-s2.0-85195375631