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Proceedings Paper

High throughput label-free optical hemogram of granulocytes enhanced by artificial neural networks (Conference Presentation)
Author(s): Roopam K. Gupta; Mingzhou Chen; Graeme P.A. Malcolm; Nils Hempler; Kishan Dholakia; Simon J. Powis

Paper Abstract

Label-free identification of immune cells presents an outstanding challenge in the current era of advanced technologies. For this, optical techniques of Raman spectroscopy and digital holographic microscopy (DHM) have been devised to successfully identify the immune cells. For accurate classification, these techniques require a post processing step of linear methods of machine learning. In this study, we show a comparison of principal component analysis and artificial neural networks for the classification of neutrophils and eosinophils based on Raman spectroscopic data and DHM based microscopic data. We show that DHM when combined with convolutional neural networks proves to be a robust, stand-alone and high throughput hemogram with a classification accuracy of 91.3% at a throughput rate of more than 100 cells per second.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11250, High-Speed Biomedical Imaging and Spectroscopy V, 112500X (10 March 2020); doi: 10.1117/12.2544563
Show Author Affiliations
Roopam K. Gupta, Univ. of St. Andrews (United Kingdom)
Mingzhou Chen, Univ. of St. Andrews (United Kingdom)
Graeme P.A. Malcolm, M Squared Lasers Ltd. (United Kingdom)
Nils Hempler, M Squared Lasers Ltd. (United Kingdom)
Kishan Dholakia, Univ. of St. Andrews (United Kingdom)
Simon J. Powis, Univ. of St. Andrews (United Kingdom)


Published in SPIE Proceedings Vol. 11250:
High-Speed Biomedical Imaging and Spectroscopy V
Kevin K. Tsia; Keisuke Goda, Editor(s)

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