dc.contributor.author |
Kaya, Yasin |
|
dc.contributor.author |
Gursoy, Ercan |
|
dc.date.accessioned |
2023-04-25T11:38:30Z |
|
dc.date.available |
2023-04-25T11:38:30Z |
|
dc.date.issued |
2023-05 |
|
dc.identifier.citation |
Kaya, Y., & Gürsoy, E. (2023). A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft Computing, 27(9), 5521-5535. https://doi.org/10.1007/s00500-022-07798-y |
tr_TR |
dc.identifier.issn |
1432-7643 |
|
dc.identifier.issn |
1433-7479 |
|
dc.identifier.uri |
http://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/4206 |
|
dc.identifier.uri |
http://dx.doi.org/10.1007/s00500-022-07798-y |
|
dc.description |
WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection. |
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dc.description.abstract |
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19. |
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dc.language.iso |
en |
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dc.publisher |
SOFT COMPUTING / SPRINGER |
tr_TR |
dc.relation.ispartofseries |
2023;Volume: 27 Issue: 9 |
|
dc.subject |
COVID-19 disease detection |
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dc.subject |
Deep transfer learning |
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dc.subject |
CNN |
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dc.subject |
Fine-tuning |
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dc.subject |
MobileNet |
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dc.title |
A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection |
tr_TR |
dc.type |
Article |
tr_TR |