Predicting Semen Motility using three-dimensional Convolutional Neural Networks
This work addresses the problem of time-consuming and error-prone manual semen analysis for fertility and IVF labs, offering an incremental improvement over existing deep learning methods.
This paper proposes using three-dimensional Convolutional Neural Networks to predict sperm motility from microscopic videos, aiming to automate semen analysis. The method achieved good results using significantly less data points from the VISEM dataset.
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.