NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY

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DOI

Łukasz Maciura

lukasz.maciura@netrix.com.pl

http://orcid.org/0000-0001-8657-3472
Dariusz Wójcik

dariusz.wojcik@netrix.com.pl

http://orcid.org/0000-0002-4200-3432
Tomasz Rymarczyk

tomasz.rymarczyk@netrix.com.pl

http://orcid.org/0000-0002-3524-9151
Krzysztof Król

krzysztof.krol@netrix.com.pl

http://orcid.org/0000-0002-0114-2794

Abstract

This paper presents a new hybrid algorithm using multiple Support Vector Machines models with convolutional autoencoder to Electrical Impedance Tomography, and Ultrasound Computed Tomography image reconstruction. The ultimate hybrid solution uses multiple SVM models to convert input measurements to individual autoencoder codes representing a given scene then the decoder part of the autoencoder can reconstruct the scene

Keywords:

convolutional autoencoder, SVM, electrical impedance tomography, ultrasound transmission tomography

References

Article Details

Maciura, Łukasz, Wójcik, D., Rymarczyk, T., & Król, K. (2023). NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(2), 4–9. https://doi.org/10.35784/iapgos.3377