Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection
Article
Article Title | Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection |
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Article Category | Article |
Authors | Sobahi, Nebras, Sengur, Abdulkadir, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 143 |
Article Number | 105335 |
Number of Pages | 10 |
Year | 2022 |
Place of Publication | United Kingdom |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105335 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482522001275 |
Abstract | Background Method Results |
Keywords | 3D CNN; Attention mechanism; ECG; Residual connections; COVID-19 detection |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | King Abdulaziz University, Saudi Arabia |
Firat University, Turkey | |
Duke-NUS Medical School, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
https://research.usq.edu.au/item/z1w29/attention-based-3d-cnn-with-residual-connections-for-efficient-ecg-based-covid-19-detection
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