dc.contributor.author | Reis, Hatice Catal | |
dc.contributor.author | Turk, Veysel | |
dc.date.accessioned | 2023-02-03T08:17:51Z | |
dc.date.available | 2023-02-03T08:17:51Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Reis, H. C., & Turk, V. (2022). COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and chest X-ray images. Artificial Intelligence in Medicine, 134 doi:10.1016/j.artmed.2022.102427 | en_US |
dc.identifier.issn | 09333657 | |
dc.identifier.uri | https://reader.elsevier.com/reader/sd/pii/S0933365722001798?token=1418D9307EAE7BD5717B69F8AF6E97058E6B0F2E9BFE81DD709CEB9CC7211EE7488CA955408F61A1DC405779DADE5DC0&originRegion=eu-west-1&originCreation=20230202110132 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12440/5751 | |
dc.description.abstract | COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier B.V. | en_US |
dc.relation.ispartof | Artificial Intelligence in Medicine | en_US |
dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
dc.subject | Chest CT-scan images | en_US |
dc.subject | Chest X-ray images | en_US |
dc.subject | COVID-DSNet | en_US |
dc.subject | Depthwise separable convolution | en_US |
dc.subject | SARS-CoV-2 | en_US |
dc.title | COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images | en_US |
dc.type | article | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Harita Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0003-2696-2446 | en_US |
dc.identifier.volume | 134 | en_US |
dc.contributor.institutionauthor | Reis, Hatice Catal | |
dc.identifier.doi | 10.1016/j.artmed.2022.102427 | en_US |
dc.authorwosid | FTB-1003-2022 | en_US |
dc.authorscopusid | 57192666861 | en_US |