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Sperm cell motility and morphology observed under the bright field microscopyare the only criteria for selecting particular sperm cell duringIntracytoplasmic Sperm Injection (ICSI) procedure of Assisted ReproductiveTechnology (ART). Several factors such as, oxidative stress, cryopreservation,heat, smoking and alcohol consumption, are negatively associated with thequality of sperm cell and fertilization potential due to the changing ofsub-cellular structures and functions which are overlooked. A bright fieldimaging contrast is insufficient to distinguish tiniest morphological cellfeatures that might influence the fertilizing ability of sperm cell. Wedeveloped a partially spatially coherent digital holographic microscope(PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normalsperm cells from sperm cells under different stress conditions such ascryopreservation, exposure to hydrogen peroxide and ethanol without anylabeling. Phase maps of 10,163 sperm cells (2,400 control cells, 2,750spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogenperoxide and ethanol respectively) are reconstructed using the data acquiredfrom PSC-DHM system. Total of seven feedforward deep neural networks (DNN) wereemployed for the classification of the phase maps for normal and stressaffected sperm cells. When validated against the test dataset, the DNN providedan average sensitivity, specificity and accuracy of 84.88%, 95.03% and 85%,respectively. The current approach DNN and QPI techniques of quantitativeinformation can be applied for further improving ICSI procedure and thediagnostic efficiency for the classification of semen quality in regards totheir fertilization potential and other biomedical applications in general.

High spatially sensitive quantitative phase imaging assisted with deep  neural network for classification of human spermatozoa under stressed  condition