Detection of COVID-19 Based on Deep Learning Methods: A Critical Review
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-43009-1_4
Reference21 articles.
1. Zhou, P., Yang, X. L., Wang, X. G., Hu, B. et al. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature.
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3. Rudra Kumar, M., Pathak, R., & Gunjan, V. K. (2022). Diagnosis and medicine prediction for COVID-19 using machine learning approach. In A. Kumar, J. M. Zurada, V. K. Gunjan & R. Balasubramanian (Eds.), Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering (Vol. 834). Singapore: Springer. https://doi.org/10.1007/978-981-16-8484-5_10.
4. Katal, A. (2023). Leveraging fog computing for healthcare. In Deep Learning Technologies for the Sustainable Development Goals: Issues and Solutions in the Post-COVID Era (pp. 51–68). Singapore: Springer Nature Singapore.
5. Karimullah, S., & Vishnu Vardhan, D. (2022). Pin density technique for congestion estimation and reduction of optimized design during placement and routing. Applied Nanoscience.
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