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In this paper, we analyse two deep learning methods to predict sperm motilityand sperm morphology from sperm videos. We use two different inputs: stackedpure frames of videos and dense optical flows of video frames. To solve thisregression task of predicting motility and morphology, stacked dense opticalflows and extracted original frames from sperm videos were used with themodified state of the art convolution neural networks. For modifications of theselected models, we have introduced an additional multi-layer perceptron toovercome the problem of over-fitting. The method which had an additionalmulti-layer perceptron with dropout layers, shows the best results when theinputs consist of both dense optical flows and an original frame of videos.

Stacked dense optical flows and dropout layers to predict sperm motility and morphology