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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F23%3A00364873" target="_blank" >RIV/68407700:21460/23:00364873 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/s23042031" target="_blank" >https://doi.org/10.3390/s23042031</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23042031" target="_blank" >10.3390/s23042031</a>

Result language

angličtina

Original language name

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

Original language description

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. 2023 by the authors.

Czech name

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Czech description

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Type

J_imp - 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

Project

<a href="/en/project/GA21-00579S" target="_blank" >GA21-00579S: Multiphysical Study of Superposition of Electromagnetic Waves in Human Head Model to Verify the Feasibility of Microwave Hyperthermia of Brain Tumors</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

23

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

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UT code for WoS article

000942248300001

EID of the result in the Scopus database

2-s2.0-85149183784