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. 2022 Mar 8;12:4132. doi: 10.1038/s41598-022-07954-2

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The proposed COVID-19 screening system. When the input scan is passed to the screening block, it is first pre-processed to remove the background artifacts. Afterward, the preprocessed scan (contain RT-LAMP sample) is passed to the proposed multi-resolution network that extracts discriminative features to differentiate COVID-19 vs. non-COVID-19 subjects. Moreover, the abbreviations within the proposed network are: ZP: Zero-padding, CONV: Convolution, BN: Batch Normalization, ReLU: Rectified Linear Unit, RB: Residual Block, MP: Max pooling, ADD: Addition, CV + ReLU: Convolution layer with ReLU activation, and FC: Fully Connected. (The figure is generated in MS Powerpoint).