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dc.contributor.author
Agnelli, Juan Pablo
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dc.contributor.author
Çöl, A.
dc.contributor.author
Lassas, M.
dc.contributor.author
Murthy, R.
dc.contributor.author
Santacesaria, M.
dc.contributor.author
Siltanen, Samuli
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dc.date.available
2021-10-13T11:33:17Z
dc.date.issued
2020-11-23
dc.identifier.citation
Agnelli, Juan Pablo; Çöl, A.; Lassas, M.; Murthy, R.; Santacesaria, M.; et al.; Classification of stroke using neural networks in electrical impedance tomography; IOP Publishing; Inverse Problems; 36; 11; 23-11-2020; 1-27
dc.identifier.issn
0266-5611
dc.identifier.uri
http://hdl.handle.net/11336/143398
dc.description.abstract
Electrical impedance tomography (EIT) is an emerging non-invasive medical imaging modality. It is based on feeding electrical currents into the patient, measuring the resulting voltages at the skin, and recovering the internal conductivity distribution. The mathematical task of EIT image reconstruction is a nonlinear and ill-posed inverse problem. Therefore any EIT image reconstruction method needs to be regularized, typically resulting in blurred images. One promising application is stroke-EIT, or classification of stroke into either ischemic or hemorrhagic. Ischemic stroke involves a blood clot, preventing blood flow to a part of the brain causing a low-conductivity region. Hemorrhagic stroke means bleeding in the brain causing a high-conductivity region. In both cases the symptoms are identical, so a cost-effective and portable classification device is needed. Typical EIT images are not optimal for stroke-EIT because of blurriness. This paper explores the possibilities of machine learning in improving the classification results. Two paradigms are compared: (a) learning from the EIT data, that is Dirichlet-to-Neumann maps and (b) extracting robust features from data and learning from them. The features of choice are virtual hybrid edge detection (VHED) functions (Greenleaf et al 2018 Anal. PDE 11) that have a geometric interpretation and whose computation from EIT data does not involve calculating a full image of the conductivity. We report the measures of accuracy, sensitivity and specificity of the networks trained with EIT data and VHED functions separately. Computational evidence based on simulated noisy EIT data suggests that the regularized grey-box paradigm (b) leads to significantly better classification results than the black-box paradigm (a).
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IOP Publishing
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CLASSIFICATION
dc.subject
EIT
dc.subject
INVERSE PROBLEMS
dc.subject
NEURAL NETWORKS
dc.subject
VHED FUNCTION
dc.subject.classification
Matemática Aplicada
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Classification of stroke using neural networks in electrical impedance tomography
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-09-06T15:09:51Z
dc.identifier.eissn
1361-6420
dc.journal.volume
36
dc.journal.number
11
dc.journal.pagination
1-27
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Agnelli, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba; Argentina
dc.description.fil
Fil: Çöl, A.. Sinop University; Turquía
dc.description.fil
Fil: Lassas, M.. University of Helsinki; Finlandia
dc.description.fil
Fil: Murthy, R.. University of Helsinki; Finlandia
dc.description.fil
Fil: Santacesaria, M.. Università degli Studi di Genova; Italia
dc.description.fil
Fil: Siltanen, Samuli. University of Helsinki; Finlandia
dc.journal.title
Inverse Problems
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1361-6420/abbdcd
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1088/1361-6420/abbdcd
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