Systematic Optimization of Training and Setting of SVM-Based Microwave Stroke Classification: Numerical Simulations for 10 Port System
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Systematic Optimization of Training and Setting of SVM-Based Microwave Stroke Classification: Numerical Simulations for 10 Port System
Original language description
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%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
ISSN
2469-7249
e-ISSN
2469-7257
Volume of the periodical
8
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
273-281
UT code for WoS article
001242894100001
EID of the result in the Scopus database
2-s2.0-85195375631