Vision Transformer-based Automatic Detection of COVID-19 in Chest X-ray Images
| dc.contributor.author | Yurdakul, Mustafa | |
| dc.contributor.author | Tasdemir, Sakir | |
| dc.date.accessioned | 2025-01-21T16:26:36Z | |
| dc.date.available | 2025-01-21T16:26:36Z | |
| dc.date.issued | 2023 | |
| dc.department | Kırıkkale Üniversitesi | |
| dc.description | 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 -- 27 December 2023 through 28 December 2023 -- Zarqa -- 201143 | |
| dc.description.abstract | The COVID-19 virus, which first emerged in the city of Wuhan in China, rapidly spread across the globe due to its high contagiousness. Detecting the virus early is crucial to stop its spread and to provide timely treatment to affected individuals. Chest X-ray (CXR) images are a quick, cost- effective, and non-invasive method commonly used for the diagnosis of COVID-19. CXR images are manually inspected by experts for diagnosis. However manually detection is not only time-consuming but also prone to errors due to human fatigue. For these reasons, there is an urgent need for a system that can detect COVID-19 from CXR images. In this study, the Vision Transformer (ViT) model was used to classify Normal, Pneumonia, and COVID-19 from CXR images. Experimental results show that the Vision Transformer (ViT) possesses a robust and high generalization capability, with an accuracy rate of 97%, indicating its significant potential in medical image analysis. © 2023 IEEE. | |
| dc.identifier.doi | 10.1109/EICEEAI60672.2023.10590260 | |
| dc.identifier.isbn | 979-835037336-3 | |
| dc.identifier.scopus | 2-s2.0-85200009750 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1109/EICEEAI60672.2023.10590260 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12587/23142 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241229 | |
| dc.subject | Classification; CNN; COVID-19; Detection; Pneumonia; Vision Transformers | |
| dc.title | Vision Transformer-based Automatic Detection of COVID-19 in Chest X-ray Images | |
| dc.type | Conference Object |
