Open Access
Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
---|---|---|
Article Number | 17 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/ijmqe/2024013 | |
Published online | 27 August 2024 |
- R. Pu, S. Liu, X. Ren, D. Shi et al., The screening value of RT-LAMP and RT-PCR in the diagnosis of COVID-19: systematic review and meta-analysis, J. Virolog. Methods 300, 114392 (2022) [CrossRef] [Google Scholar]
- V.T. Chu, N.G. Schwartz, M.A. Donnelly et al., Comparison of home antigen testing with RT-PCR and viral culture during the course of SARS-CoV-2 infection, JAMA Intern. Med. 182, 701–709 (2022) [CrossRef] [PubMed] [Google Scholar]
- L. Wynants, B. Van Calster, G.S. Collins et al., Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, BMJ 369, 1328 (2020) [Google Scholar]
- R. Kumar, R. Arora, V. Bansal et al., Accurate prediction of COVID-19 using chest X-ray images through deep feature learning model with SMOTE and machine learning classifiers, MedRxiv 2020–04 (2020) [Google Scholar]
- S.H. Kassania, P.H. Kassanib, M.J. Wesolowskic, K.A. Schneidera, R. Detersa, Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach, Biocybern. Biomed. Eng. 41, 867–879 (2021) [CrossRef] [Google Scholar]
- X. Jiang, M. Coffee, A. Bari et al., Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity, Comput. Mater. Continu. 63, 537–51 (2020) [CrossRef] [Google Scholar]
- A.F. de Moraes Batista, J.L. Miraglia, T.H.R. Donato, A.D.P. Chiavegatto Filho, COVID-19 diagnosis prediction in emergency care patients: a machine learning approach, medRxiv 2020–04 (2020) [Google Scholar]
- T.B. Alakusv, I. Turkoglu, Comparison of deep learning approaches to predict COVID-19 infection, Chaos Solitons Fract. 140, 110120 (2020) [CrossRef] [Google Scholar]
- C. Farabet, C. Couprie, L. Najman, Y. LeCun, Learning hierarchical features for scene labelling, IEEE Trans. Pattern Anal. Machine Intell. 35, 1915–1929 (2012) [Google Scholar]
- D.C. Ciresan, U. Meier, J. Masci et al., Flexible, high performance convolutional neural networks for image classification. Switzerland, in Twenty-second International Joint Conference on Artificial Intelligence (2011) [Google Scholar]
- N. Kouiroukidis, G. Evangelidis, The effects of dimensionality curse in high dimensional kNN search, in 15th Panhellenic Conference on Informatics, Kastoria, Greece (2011). pp. 41–45 [Google Scholar]
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