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dc.rights.licenseVisos teisės saugomos / All rights reserveden_US
dc.contributor.authorCicėnas, Benediktas
dc.contributor.authorAbromavičius, Vytautas
dc.date.accessioned2025-12-18T14:06:31Z
dc.date.available2025-12-18T14:06:31Z
dc.date.issued2022
dc.identifier.isbn9781665450492en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/159596
dc.description.abstractWith the advance of Covid-19 pneumonia and its complications increased the load of radiologists several times. The pneumonia detection usually is performed by examination of chest X-Ray radiograph by the radiologists. This process is tedious, influenced by fatigue and may lead to mistakes. Computer-aided diagnostic systems shows the potential for improving accuracy. In this work, we present investigation for pneumonia detection based on several convolutional neural network architectures via transfer learning. Additionally, we propose a method for preparing data from the Radiological Society of North America Pneumonia Detection Challenge to achieve better results.en_US
dc.format.extent4 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/159399en_US
dc.source.urihttps://ieeexplore.ieee.org/document/9781683en_US
dc.subjectConvolutional neural networksen_US
dc.subjectDiseasesen_US
dc.subjectMachine learningen_US
dc.subjectPneumoniaen_US
dc.subjectX-Raysen_US
dc.titleInvestigation of Pneumonia Detection using Convolutional Neural Networksen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2022-05-30
dcterms.references15en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionVilniaus Gedimino technikos universitetasen_US
dc.contributor.institutionVilnius Gediminas Technical Universityen_US
dc.contributor.departmentElektroninių sistemų katedra / Department of Electronic Systemsen_US
dcterms.sourcetitle2022 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 21, 2022, Vilnius, Lithuaniaen_US
dc.identifier.eisbn9781665450485en_US
dc.identifier.eissn2690-8506en_US
dc.publisher.nameIEEEen_US
dc.publisher.countryUnited States of Americaen_US
dc.publisher.cityNew Yorken_US
dc.identifier.doihttps://doi.org/10.1109/eStream56157.2022.9781683en_US


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