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