Rapid bacteria identification using structured illumination microscopy and machine learning

Yingchuan He; Weize Xu; Yao Zhi; Rohit Tyagi; Zhe Hu; Gang Cao

doi:10.1142/s1793545818500074

[1] S. C. Gopinath, T. H. Tang, Y. Chen, M. Citartan, T. Lakshmipriya, “Bacterial detection: From microscope to smartphone," Biosens. Bioelectron. 60, 332-342 (2014).

[2] J. E. Clarridge III, “Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases," Clin. Microbiol. Rev. 17(4), 840-862 (2004).

[3] S. A. Bandh, A. N. Kamili, B. A. Ganai, “Identification of some Penicillium species by traditional approach of morphological observation and culture," Afr. J. Microbiol. Res. 5(21), 3493-3496 (2011).

[4] B. Huang, H. Babcock, X. Zhuang, “Breaking the diffraction barrier: Super-resolution imaging of cells," Cell 143(7), 1047-1058 (2010).

[5] A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks," Nature 542(7639), 115-118 (2017).

[6] J. L. Alonso, S. Mascellaro, Y. Moreno, M. A. Ferrus, J. Hernandez, “Double-staining method for differentiation of morphological changes and membrane integrity of Campylobacter coli cells," Appl. Environ. Microbiol. 68(10), 5151-5154 (2002).

[7] D. L. Pham, C. Xu, J. L. Prince, “Current methods in medical image segmentation, Annu. Rev. Biomed. Eng. 2(1), 315-337 (2000).

[8] G. Bradski, “The OpenCV Library," Dr. Dobb's J. 25(11), 120-123 (2000).

[9] W. Zhao, R. Chellappa, A. Krishnaswamy, “Discriminant analysis of principal components for face recognition," Third IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 336 341, IEEE, New Year (1998).

[10] C. Cortes, V. Vapnik, “Support vector machine," Mach. Learn. 20(3), 273-297 (1995).

[11] L. E. Peterson, “K-nearest neighbor," Scholarpedia 4(2), 1883 (2009).

[12] A. Liaw, M. Wiener, “Classification and regression by random forest," R News 2(3), 18-22 (2002).

[13] T. M. Khoshgoftaar, M. Golawala, J. Van Hulse, “An empirical study of learning from imbalanced data using random forest," 19th IEEE Int. Conf. Tools with Artificial Intelligence, Vol. 2, pp. 310-317, IEEE, New York (2007).

[14] J. Weston, C. Watkins, “Support vector machines for multi-class pattern recognition," ESANN 99, 219-224 (1999).

[15] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, “Scikit-learn: Machine learning in Python," J. Mach. Learn. Res. 12(October), 2825-2830 (2011).

[16] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection," IJCAI 14(2), 1137-1145 (1995).