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Proceedings Paper

CNN based classification of 5 cell types by diffraction images
Author(s): Jiahong Jin; Jun Q. Lu; Yuhua Wen; Peng Tian; Xin-Hua Hu
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Paper Abstract

Rapid and label-free cell assay presents a challenging and significant problem that have wide applications in life science and clinics. We report here a method that combines polarization diffraction imaging flow cytometry (p-DIFC) with deep convolutional neural network (CNN) based image analysis for solving the above problem. Cross-polarized diffraction image (p-DI) pairs were acquired from 6185 cells in 5 types to investigate their uses for accurate classification. Different CNN architects have been studied to develop a compact architect named DINet which has relatively small set of network parameter for fast training and test. The averaged accuracy among the 5 groups of p-DI data ranges from 98.7% to 99.2%. With the DINet, the strong potentials of the p-DIFC method for morphology based and label-free cell assay have been demonstrated.

Paper Details

Date Published: 30 July 2019
PDF: 3 pages
Proc. SPIE 11076, Advances in Microscopic Imaging II, 110761F (30 July 2019);
Show Author Affiliations
Jiahong Jin, Hunan Institute of Science and Technology (China)
East Carolina Univ. (United States)
Jun Q. Lu, Hunan Institute of Science and Technology (China)
East Carolina Univ. (United States)
Yuhua Wen, Hunan Institute of Science and Technology (China)
Peng Tian, Hunan Institute of Science and Technology (China)
Xin-Hua Hu, Hunan Institute of Science and Technology (China)
East Carolina Univ. (United States)


Published in SPIE Proceedings Vol. 11076:
Advances in Microscopic Imaging II
Emmanuel Beaurepaire; Francesco Saverio Pavone; Peter T. C. So, Editor(s)

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