# Predicting Semen Motility using three-dimensional Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2101.02888 · HTML: https://mlsanity.com/paper/2101.02888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction](https://mlsanity.com/paper/1910.13327/) (2019)
- [Stacked dense optical flows and dropout layers to predict sperm motility and morphology](https://mlsanity.com/paper/1911.03086/) (2019)
- [Extracting temporal features into a spatial domain using autoencoders for sperm video analysis](https://mlsanity.com/paper/1911.03100/) (2019)
- [Sperm Detection and Tracking in Phase-Contrast Microscopy Image Sequences using Deep Learning and Modified CSR-DCF](https://mlsanity.com/paper/2002.04034/) (2020)

## Abstract (arXiv)

Manual and computer aided methods to perform semen analysis are time-consuming, requires extensive training and prone to human error. The use of classical machine learning and deep learning based methods using videos to perform semen analysis have yielded good results. The state-of-the-art method uses regular convolutional neural networks to perform quality assessments on a video of the provided sample. In this paper we propose an improved deep learning based approach using three-dimensional convolutional neural networks to predict sperm motility from microscopic videos of the semen sample. We make use of the VISEM dataset that consists of video and tabular data of semen samples collected from 85 participants. We were able to achieve good results from significantly less data points. Our models indicate that deep learning based automatic semen analysis may become a valuable and effective tool in fertility and IVF labs.