Automated sickle cell disease identification in human red blood cells using a lensless single random phase encoding biosensor and convolutional neural networks

We present a compact, field portable, lensless, single random phase encoding biosensor for automated classification between healthy and sickle cell disease human red blood cells. Microscope slides containing 3 mu l wet mounts of whole blood samples from healthy and sickle cell disease afflicted human donors are input into a lensless single random phase encoding (SRPE) system for disease identification. A partially coherent laser source (laser diode) illuminates the cells under inspection wherein the object complex amplitude propagates to and is pseudorandomly encoded by a diffuser, then the intensity of the diffracted complex waveform is captured by a CMOS image sensor. The recorded opto-biological signatures are transformed using local binary pattern map generation during preprocessing then input into a pretrained convolutional neural network for classification between healthy and disease-states. We further provide analysis that compares the performance of several neural network architectures to optimize our classification strategy. Additionally, we assess the performance and computational savings of classifying on subsets of the opto-biological signatures with substantially reduced dimensionality, including one dimensional cropping of the recorded signatures. To the best of our knowledge, this is the first report of a lensless SRPE biosensor for human disease identification. As such, the presented approach and results can be significant for low-cost disease identification both in the field and for healthcare systems in developing countries which suffer from constrained resources. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

This study introduces a compact, lensless, single random phase encoding biosensor for automated classification between healthy and sickle cell disease human red blood cells. Disease identification is achieved through opto-biological signatures transformation and convolutional neural network classification, showing potential for low-cost disease identification in resource-constrained settings.