Machine learning-based screening of red blood cells using quantitative phase imaging with micro-spectrocolorimetry

We report simultaneous micro-spectrocolorimetry and quantitative phase imaging (QPI) of human red blood cells (RBCs) using white light interference microscopy. To understand the mechanism of disease at the cellular level and to increase the diagnostic performance of QPI, we integrated it with micro-spectrocolorimetry. Color-coordinates of various spatial locations of RBCs along with 3D phase-maps help to quantify different biophysical parameters. Features extracted from this multimodal technique combined with support vector machine achieved average specificity, sensitivity, and accuracy of 95.52%, 95.58%, and 95.55%, respectively with testing data in classification of healthy and unhealthy RBCs. A better result is obtained by using synergies amongst data as compared to QPI based features only.