| dc.rights.license | Visos teisės saugomos / All rights reserved | en_US |
| dc.contributor.author | Cicėnas, Benediktas | |
| dc.contributor.author | Abromavičius, Vytautas | |
| dc.date.accessioned | 2025-12-18T14:06:31Z | |
| dc.date.available | 2025-12-18T14:06:31Z | |
| dc.date.issued | 2022 | |
| dc.identifier.isbn | 9781665450492 | en_US |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/159596 | |
| dc.description.abstract | With 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.extent | 4 p. | en_US |
| dc.format.medium | Tekstas / Text | en_US |
| dc.language.iso | en | en_US |
| dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/159399 | en_US |
| dc.source.uri | https://ieeexplore.ieee.org/document/9781683 | en_US |
| dc.subject | Convolutional neural networks | en_US |
| dc.subject | Diseases | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Pneumonia | en_US |
| dc.subject | X-Rays | en_US |
| dc.title | Investigation of Pneumonia Detection using Convolutional Neural Networks | en_US |
| dc.type | Konferencijos publikacija / Conference paper | en_US |
| dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
| dcterms.issued | 2022-05-30 | |
| dcterms.references | 15 | en_US |
| dc.description.version | Taip / Yes | en_US |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | en_US |
| dc.contributor.institution | Vilnius Gediminas Technical University | en_US |
| dc.contributor.department | Elektroninių sistemų katedra / Department of Electronic Systems | en_US |
| dcterms.sourcetitle | 2022 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 21, 2022, Vilnius, Lithuania | en_US |
| dc.identifier.eisbn | 9781665450485 | en_US |
| dc.identifier.eissn | 2690-8506 | en_US |
| dc.publisher.name | IEEE | en_US |
| dc.publisher.country | United States of America | en_US |
| dc.publisher.city | New York | en_US |
| dc.identifier.doi | https://doi.org/10.1109/eStream56157.2022.9781683 | en_US |