Serena Low, Woan Ching and Chuah, Joon Huang and Tee, Clarence Augustine T. H. and Anis, Shazia and Shoaib, Muhammad Ali and Faisal, Amir and Khalil, Azira and Lai, Khin Wee (2021) An overview of deep learning techniques on chest X-ray and CT Scan identification of COVID-19. Computational and Mathematical Methods in Medicine, 2021. ISSN 1748-670X, DOI https://doi.org/10.1155/2021/5528144.
Full text not available from this repository.Abstract
Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
| Item Type: | Article |
|---|---|
| Funders: | RU Geran University of Malaya [ST014-2019] |
| Uncontrolled Keywords: | Convolutional Neural-Network; Pneumonia detection; Coronavirus; CNN |
| Subjects: | Q Science > QA Mathematics Q Science > QD Chemistry Q Science > QH Natural history |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 01 Aug 2022 02:14 |
| Last Modified: | 01 Aug 2022 02:14 |
| URI: | http://eprints.um.edu.my/id/eprint/33915 |
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