Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images
| dc.contributor.author | Duru, Dilek Göksel | |
| dc.contributor.author | Duru, Adil Deniz | |
| dc.contributor.author | Uçan, Osman Nuri | |
| dc.contributor.author | Al-azzaw, Athar | |
| dc.contributor.author | Al-jumaili, Saif | |
| dc.date.accessioned | 2024-04-04T21:06:26Z | |
| dc.date.available | 2024-04-04T21:06:26Z | |
| dc.date.issued | 2023 | |
| dc.department | TAÜ, Fen Fakültesi, Moleküler Biyoteknoloji Bölümü | en_US |
| dc.description.abstract | In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV-2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew’s correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID19. | |
| dc.identifier.citation | Duru, Dilek G., Duru, Adil D., Uçan, Osman N., Al-azzaw, A., Al-jumaili, S. (2023). Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images. International Information and Engineering Technology Association, 40 (2), 407-420. | |
| dc.identifier.doi | 10.18280/ts.400201 | |
| dc.identifier.endpage | 420 | en_US |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopus | 2-s2.0-85162086251 | |
| dc.identifier.startpage | 407 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12846/1039 | |
| dc.identifier.volume | 40 | en_US |
| dc.identifier.wos | WOS:000996210200001 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | International Information and Engineering Technology Association | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Deep learning | en_US |
| dc.subject | Classification | en_US |
| dc.subject | CNN | en_US |
| dc.subject | X-ray | en_US |
| dc.subject | CT scan | en_US |
| dc.title | Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images | |
| dc.type | Article |
