| dc.contributor.author | Cicėnas, Benediktas | |
| dc.contributor.author | Abromavičius, Vytautas | |
| dc.date.accessioned | 2023-09-18T16:19:34Z | |
| dc.date.available | 2023-09-18T16:19:34Z | |
| dc.date.issued | 2022 | |
| dc.identifier.other | (SCOPUS_ID)85132194074 | |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/113207 | |
| 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. | eng |
| dc.format | PDF | |
| dc.format.extent | p. 1-4 | |
| dc.format.medium | tekstas / txt | |
| dc.language.iso | eng | |
| dc.relation.isreferencedby | Conference Proceedings Citation Index - Science (Web of Science) | |
| dc.relation.isreferencedby | Scopus | |
| dc.relation.isreferencedby | IEEE Xplore | |
| dc.rights | Laisvai prieinamas internete | |
| dc.source.uri | https://ieeexplore.ieee.org/document/9781683 | |
| dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:135820667/datastreams/MAIN/content | |
| dc.title | Investigation of pneumonia detection using convolutional neural networks | |
| dc.type | Straipsnis konferencijos darbų leidinyje Web of Science DB / Paper in conference publication in Web of Science DB | |
| dcterms.references | 15 | |
| dc.type.pubtype | P1a - Straipsnis konferencijos darbų leidinyje Web of Science DB / Article in conference proceedings Web of Science DB | |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
| dc.contributor.faculty | Elektronikos fakultetas / Faculty of Electronics | |
| dc.subject.researchfield | T 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering | |
| dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
| dc.subject.studydirection | B04 - Informatikos inžinerija / Informatics engineering | |
| dc.subject.studydirection | E09 - Elektronikos inžinerija / Electronic engineering | |
| dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
| dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
| dc.subject.en | convolutional neural networks | |
| dc.subject.en | diseases | |
| dc.subject.en | machine learning | |
| dc.subject.en | pneumonia | |
| dc.subject.en | X-Rays | |
| dcterms.sourcetitle | 2022 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 21 April 2022, Vilnius, Lithuania / organized by: Vilnius Gediminas Technical University | |
| dc.publisher.name | IEEE | |
| dc.publisher.city | Piscataway, NJ | |
| dc.identifier.doi | 2-s2.0-85132194074 | |
| dc.identifier.doi | 85132194074 | |
| dc.identifier.doi | 0 | |
| dc.identifier.doi | 137608473 | |
| dc.identifier.doi | 000848697000004 | |
| dc.identifier.doi | 10.1109/eStream56157.2022.9781683 | |
| dc.identifier.elaba | 135820667 | |