# A Machine Learning Framework for Automatic Prediction of Human Semen Motility

arXiv: https://arxiv.org/abs/2109.08049 · HTML: https://mlsanity.com/paper/2109.08049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 59 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Convolutional Neural Network-based Place Recognition](https://mlsanity.com/paper/1411.1509/) (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)

## Built on by (most cited first, 1 of 1)

- [Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset](https://mlsanity.com/paper/2607.08429/) (2026)

## Abstract (arXiv)

In this paper, human semen samples from the visem dataset collected by the Simula Research Laboratory are automatically assessed with machine learning methods for their quality in respect to sperm motility. Several regression models are trained to automatically predict the percentage (0 to 100) of progressive, non-progressive, and immotile spermatozoa in a given sample. The video samples are adopted for three different feature extraction methods, in particular custom movement statistics, displacement features, and motility specific statistics have been utilised. Furthermore, four machine learning models, including linear Support Vector Regressor (SVR), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), have been trained on the extracted features for the task of automatic motility prediction. Best results for predicting motility are achieved by using the Crocker-Grier algorithm to track sperm cells in an unsupervised way and extracting individual mean squared displacement features for each detected track. These features are then aggregated into a histogram representation applying a Bag-of-Words approach. Finally, a linear SVR is trained on this feature representation. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the Mean Absolute Error (MAE) could be reduced from 8.83 to 7.31. For the sake of reproducibility, we provide the source code for our experiments on GitHub.