Improving Coronavirus (COVID-19) Diagnosis Using Deep Transfer Learning
15th International Conference on Information Technology and Applications, ICITA 2021
; 350:23-37, 2022.
Article
in English
| Scopus | ID: covidwho-1844318
ABSTRACT
Background:
Coronavirus disease (COVID-19) is an infectious dis- ease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.Aim:
The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia, and healthy cases using deep learning techniques.Method:
In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance of different CNN architectures.Results:
Evaluation results using K-fold (10) showed that we have achieved state-of-the-art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.Conclusion:
Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models developed in this study could be used for early screening of coronavirus;however, it calls for extensive need to CT or X-rays dataset to develop a reliable application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
15th International Conference on Information Technology and Applications, ICITA 2021
Year:
2022
Document Type:
Article
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