Abstract
We overview a previously reported system for automated diagnosis of sickle cell disease based on red blood cell (RBC) membrane fluctuations measured via digital holographic microscopy. A low-cost, compact, 3D-printed shearing interferometer is used to record video holograms of RBCs. Each hologram frame is reconstructed in order to form a spatio-temporal data cube from which features regarding membrane fluctuations are extracted. The motility-based features are combined with static morphology-based cell features and inputted into a random forest classifier which outputs the disease state of the cell with high accuracy.
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