Gold-viral particle identification by deep learning in wide-field photon scattering parametric images

Zhao, Hanwen, Ni, Bin, Jin, Xiao, Zhang, Heng, Hou, Jamie Jiangmin, Hou, Lianping, Marsh, John H., Dong, Lei, Li, Shanhu, Gao, Xiaohong W. ORCID logoORCID:, Shi, Daming, Liu, Xuefeng and Xiong, Jichuan (2022) Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. Applied Optics, 61 (2) . pp. 546-553. ISSN 1559-128X [Article] (doi:10.1364/AO.445953)

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The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution instruments such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are required. In this paper, we propose a new method for identifying virus particles based on polarization parametric indirect microscopic imaging (PIMI) and deep learning techniques. By introducing an abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), the strength of the photon scattering signal can be magnified. After acquiring the PIMI images, a deep learning method was applied to identify discriminating features and classify the virus particles, using electron microscopy (EM) images as the ground truth. Experimental results confirm that gold-virus particles can be identified in PIMI images with a high level of confidence.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 34581
Notes on copyright: © 2022 Optica Publishing Group.
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Depositing User: Xiaohong Gao
Date Deposited: 26 Jan 2022 10:36
Last Modified: 07 Jan 2023 04:04

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