CNN based classification of 5 cell types by diffraction images

Jiahong Jin; Jun Q. Lu; Yuhua Wen; Peng Tian; Xin-Hua Hu

doi:10.1117/12.2526892

30 July 2019 CNN based classification of 5 cell types by diffraction images

Jiahong Jin, Jun Q. Lu, Yuhua Wen, Peng Tian, Xin-Hua Hu

Author Affiliations +

Proceedings Volume 11076, Advances in Microscopic Imaging II; 110761F (2019) https://doi.org/10.1117/12.2526892
Event: European Conferences on Biomedical Optics, 2019, Munich, Germany

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.

Citation Download Citation

Jiahong Jin, Jun Q. Lu, Yuhua Wen, Peng Tian, and Xin-Hua Hu "CNN based classification of 5 cell types by diffraction images", Proc. SPIE 11076, Advances in Microscopic Imaging II, 110761F (30 July 2019); https://doi.org/10.1117/12.2526892

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KEYWORDS

Diffraction

Light scattering

Image classification

Flow cytometry

Polarization

Convolutional neural networks

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