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- Title
Emergency COVID-19 detection from chest X-rays using deep neural networks and ensemble learning.
- Authors
Jouibari, Zahra Ebrahimi; Moakhkhar, Hosein Navaei; Baleghi, Yasser
- Abstract
While several papers have explored the application of deep learning for COVID-19 detection in chest X-ray images, the consideration of images taken under emergency conditions remains limited. In this paper, we define emergency conditions as situations where inevitable errors occur during X-ray imaging in severe respiratory conditions of COVID-19 patients. These errors include positioning errors, protocol parameter errors, and motion artifacts. Our research aims to propose an intelligent tool based on deep neural networks and ensemble learning to detect COVID-19 cases from chest X-ray images in both standard and emergency conditions. As comprehensive datasets incorporating emergency images are scarce, we first generated a local dataset by investigating COVID-19 cases in our local hospital. We specifically selected cases with the aforementioned errors caused by emergency conditions. This dataset comprises patients in acute situations, as well as technical radiographic errors resulting from a significant number of people requiring radiography images. We evaluated the performance of recent popular pre-trained deep neural networks on our local dataset, which highlighted the importance of considering such emergency images. Subsequently, we transfer-learned 11 popular deep neural networks using our local dataset. Furthermore, we identified the transferred neural networks with the highest accuracies (exceeding 95% accuracy) and implemented ensemble learning methods such as Majority Voting and Error-Correcting Output Codes (ECOC). Our proposed methods excel in identifying COVID-19 in both emergency and non-emergency chest X-rays with high accuracy.
- Subjects
ARTIFICIAL neural networks; X-rays; COVID-19 pandemic; X-ray imaging; COVID-19; ERROR-correcting codes
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 17, p52141
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-17508-x