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

G. Gupta; T. O'Connor; B. Javidi

doi:10.1117/12.3013770

7 June 2024 Overview of digital holographic deep learning of red blood cells for field-portable, rapid disease screening

G. Gupta, T. O'Connor, B. Javidi

Author Affiliations +

Proceedings Volume 13041, Three-Dimensional Imaging, Visualization, and Display 2024; 130410K (2024) https://doi.org/10.1117/12.3013770
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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G. Gupta, T. O'Connor, and B. Javidi "Overview of digital holographic deep learning of red blood cells for field-portable, rapid disease screening", Proc. SPIE 13041, Three-Dimensional Imaging, Visualization, and Display 2024, 130410K (7 June 2024); https://doi.org/10.1117/12.3013770

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