@article{Siddiqui_S-2022_91936,
title = {Deep learning models for the diagnosis and screening of COVID-19: a systematic review.},
author = {Siddiqui, S. and Arifeen, M. and Hopgood, A. and Good, A. and Gegov, A. and Hossain, E. and Rahman, W. and Hossain, S. and Al Jannat, S. and Ferdous, R. and Masum, S.},
month = {jul},
year = {2022},
abstract = {COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identifcation. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classifcation and identifcation from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.},
volume = {3},
issue = {5},
journal = {SN Computer Science},
publisher = {Springer Nature},
url = {https://doi.org/10.1007/s42979-022-01326-3},
}
