Enhanced Image Reconstruction in Electrical Impedance Tomography using Radial Basis Function Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU154841" target="_blank" >RIV/00216305:26220/24:PU154841 - isvavai.cz</a>

Result on the web

<a href="https://www.measurement.sk/2024/p.html#No.%206" target="_blank" >https://www.measurement.sk/2024/p.html#No.%206</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/msr-2024-0027" target="_blank" >10.2478/msr-2024-0027</a>

Result language

angličtina

Original language name

Enhanced Image Reconstruction in Electrical Impedance Tomography using Radial Basis Function Neural Networks

Original language description

This paper presents a novel cascade algorithm for image reconstruction in electrical impedance tomography (EIT) using radial basis function neural networks. The first subnetwork applies a density-based algorithm and k-nearest neighbors (KNN) to determine the center and width of the radial basis function neural networks, with the aim of preventing ill-conditioned connection weights between the hidden and output layers. The second subnetwork is a generalized regression neural network dedicated to functional approximation. The combined subnetworks result in a reduced mean square error and achieve an accuracy of 89.54 % without noise and an accuracy between 82.90 % and 89.53 % with noise levels ranging from 30 to 60 dB. In comparison, the original radial basis function neural networks (RBFNN) method achieves an accuracy of 85.44 % without noise and between 80.90 % and 85.31 % under similar noise conditions. The total variation (TV) method achieves 83.13 % without noise, with noise-influenced accuracy ranging from 34.28 % to 45.15 %. The Gauss-Newton method achieves 82.35 % accuracy without noise, with accuracy ranging from 33.21 % to 46.15 % in the presence of noise. The proposed method proves to be resilient to various types of noise, including white Gaussian noise, impulsive noise, and contact noise, and consistently delivers superior performance. It also outperforms the other methods in noise-free conditions. The reliability of the method in noisy environments supports its potential application in the development of new modular systems for electrical impedance tomography.

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

20201 - Electrical and electronic engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Measurement Science Review

ISSN

1335-8871

e-ISSN

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Volume of the periodical

neuveden

Issue of the periodical within the volume

6

Country of publishing house

SK - SLOVAKIA

Number of pages

11

Pages from-to

200-210

UT code for WoS article

001382280700007

EID of the result in the Scopus database

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