Comparative study for automated coronavirus detection in CT images with transfer learning
| Autor: | Nisreen Sulayman |
|---|---|
| Jazyk: | Arabic<br />English |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | مجلة جامعة دمشق للعلوم الهندسية, Vol 38, Iss 4 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1999-7302 2789-6854 |
| Popis: | Purpose: To design a computer-aided diagnosis system with transfer learning methods to serve as decision support system for automated coronavirus detection in CT images. Methods: Four pre-trained deep convolutional neural networks (ResNet-18, SqueezeNet, ShuffleNet, MobileNet-v2) have been investigated to diagnose coronavirus with CT scans. To evaluate the pre-trained deep convolutional neural network, we used the COVID-CT dataset, which contains 349 CT images of COVID-19 from 216 patients, and 397 CT images obtained from non-COVID-19 subjects. Results: Considering binary classification performance results, it has been seen that the pre-trained ResNet-18 model provides the highest classification performance (97.0470 ± 5.5466 accuracy, 98.7342 ± 2.1925 sensitivity, 95.1429 ± 9.3460 specificity, and 0.9737± 0.0489 F1-score) among other three used models. Conclusion: ResNet-18 model can be employed as a supportive decision-making system to assist radiologists at clinics and hospitals to screen patients swiftly. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|