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USING NEURAL NETWORKS AND DEEP LEARNING ALGORITHMS IN ELECTRICAL IMPEDANCE TOMOGRAPHY

cris.lastimport.scopus2024-09-19T01:30:54Z
dc.abstract.enThis paper refers to the cases of the use of Artificial Neural Networks and Convolutional Neural Networks in impedance tomography. Machine Learning methods can be used to teach computers different technical problems. The efficient use of conventional artificial neural networks in tomography is possible able to effectively visualize objects. The first step of implementation Deep Learning methods in Electrical Impedance Tomography was performed in this work.
dc.contributor.authorGrzegorz Kłosowski
dc.contributor.authorTomasz Rymarczyk
dc.date.accessioned2024-05-07T12:11:07Z
dc.date.available2024-05-07T12:11:07Z
dc.date.issued2017
dc.identifier.doi10.5604/01.3001.0010.5226
dc.identifier.issn2083-0157
dc.identifier.issn2391-6761
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/284
dc.languageen
dc.relation.ispartofInformatics Control Measurement in Economy and Environment Protection
dc.rightsCC-BY-SA
dc.subject.enImaging tomography
dc.subject.enMultilayer Perceptron
dc.subject.enDeep Learning
dc.subject.enConvolutional Neural Networks
dc.titleUSING NEURAL NETWORKS AND DEEP LEARNING ALGORITHMS IN ELECTRICAL IMPEDANCE TOMOGRAPHY
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue3
oaire.citation.volume7