A Comprehensive Review of Algorithms Developed for Rapid Pathogen Detection and Surveillance

Author:

Zar Amna,Zar Lubna,Mohsen Sara,Magdi Yosra,Zughaier Susu M.

Publisher

Springer Nature Switzerland

Reference85 articles.

1. C. Institute of Medicine Committee on Emerging Microbial Threats to Health in the 21st, in Microbial Threats to Health: Emergence, Detection, and Response, ed. by M.S. Smolinski, M.A. Hamburg, J. Lederberg, (National Academies Press (US). Copyright 2003 by the National Academy of Sciences. All rights reserved, Washington, DC, 2003)

2. N.A. Mohamad, N.A. Jusoh, Z.Z. Htike, S.L. Win, Bacteria identification from microscopic morphology using naïve bayes. Int. J. Comput. Eng. Inf. Technol. 4(2), 1–9 (2014)

3. V.R. Vijaykumar, Classifying bacterial species using computer vision and machine learning. Int. J. Comput. Appl. 151, 23–26 (2016)

4. E.A. Hay, R. Parthasarathy, Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets. PLoS Comput. Biol. 14(12), e1006628 (2018). https://doi.org/10.1371/journal.pcbi.1006628

5. T. Ahmed, M.F. Wahid, M.J. Hasan, Combining deep convolutional neural network with support vector machine to classify microscopic bacteria images, in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), (7–9 Feb 2019, 2019), pp. 1–5. https://doi.org/10.1109/ECACE.2019.8679397

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