Overview of digital holographic deep learning of red blood cells for field-portable, rapid disease screening

In this work, we present an overview of previously published work on the identification of COVID-19 red blood cells (RBCs) and sickle cell disease based on the reconstructed phase profile using a deep learning framework. The video holograms for thin blood smears were recorded using a compact, low-cost, and field portable, 3D-printed shear-based digital holographic system. Individual cells were segmented from the holograms and then each frame was reconstructed to extract spatio-temporal signatures of the cells. Morphology-based features along with motility-based features extracted from reconstructed phase images, were fed to a bi-LSTM to classify between COVID-19 positive and healthy red blood cells. Based on the majority of the cell subjects were classified as healthy or diseased.