Compressive raman classification of cells using a neural network for optical filter design and cell classification

Abstract

A method for cell classification performs a compressive Raman measurement of a cell sample that involves frequency filtering the dispersed optical signal by a tunable optical filter whose frequency response is defined by weights that were derived from trained weights of a first hidden layer of a neural network trained on Raman spectra of cells and corresponding labels. The frequency filtered signals are detected by an optical detector to produce a compressive Raman measurement. Multiple compressive Raman measurements are then input to a prediction neural network to predict a label of the cell sample. The prediction neural network is the same as the trained neural network except without the input layer and the weights of the first hidden layer.