An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to identify immune cell subsets. To achieve high accuracy, these techniques require a post-processing step using linear methods of multivariate processing, such as principal component analysis. Here we demonstrate for the first time a comparison between artificial neural networks and principal component analysis (PCA) to classify the key granulocyte cell lineages of neutrophils and eosinophils using both digital holographic microscopy and Raman spectroscopy. Artificial neural networks can offer advantages in terms of classification accuracy and speed over a PCA approach. We conclude that digital holographic microscopy with convolutional neural networks based analysis provides a route to a robust, stand-alone and high-throughput hemogram with a classification accuracy of 91.3 % at a throughput rate of greater than 100 cells per second.
|Number of pages||15|
|Publication status||Published - 2019|
Bibliographical noteFunding Information:
This work was supported by a Medical Research Scotland PhD studentship awarded to R.K.G, and grant funding from the UK Engineering and Physical Sciences Research Council (grants EP/R004854/1, EP/P030017/1). The opinions expressed in this article are the authors own and do not reflect the view of above mentioned funding agencies. KD and SJP developed the project. RKG developed the numerical analysis procedures presented and performed the experiments with MC and SJP. RKG, MC, SJP and KD wrote the paper which was approved by NH and GPAM. KD, SJP and NH supervised the project. R. Gupta, M. Chen, G. P. A. Malcolm, N. Hempler, K. Dholakia, and S. J. Powis provide the following: Data Underpinning: A label-free optical hemogram of granulocytes enhanced by artificial neural networks, Dataset, University of St Andrews Research Portal, https://doi.org/10.17630/c3b0856b-3400-4211-9e7c-eacfc7082067.
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All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics