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In this paper, we present a two-step deep learning method that is used topredict sperm motility and morphology-based on video recordings of humanspermatozoa. First, we use an autoencoder to extract temporal features from agiven semen video and plot these into image-space, which we callfeature-images. Second, these feature-images are used to perform transferlearning to predict the motility and morphology values of human sperm. Thepresented method shows it's capability to extract temporal information intospatial domain feature-images which can be used with traditional convolutionalneural networks. Furthermore, the accuracy of the predicted motility of a givensemen sample shows that a deep learning-based model can capture the temporalinformation of microscopic recordings of human semen.

Extracting temporal features into a spatial domain using autoencoders for sperm video analysis