High throughput label-free optical hemogram of granulocytes enhanced by artificial neural networks (Conference Presentation)

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.