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Review
. 2023 Feb 9;14(2):451.
doi: 10.3390/genes14020451.

Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset

Michal Dobrovolny  1 , Jakub Benes  1 , Jaroslav Langer  1 , Ondrej Krejcar  1   2 , Ali Selamat  1   2   3
Affiliations
Review

Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset

Michal Dobrovolny et al. Genes (Basel). .

Abstract

Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

Keywords: computer-aided sperm analysis; small-object detection; sperm-cell detection; yolo.

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Conflict of interest statement

The authors declare that they have no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Yearly count of articled published on the Web of Science.
Figure 2
Figure 2
Keyword analysis.
Figure 3
Figure 3
Example image extracted from dataset video [42].
Figure 4
Figure 4
Explained labels and their sizes as width and height of boxes. Plotted with seaborn package.
Figure 5
Figure 5
The network architecture of YOLOv5.
Figure 6
Figure 6
Comparison for validation dataset labels of (a,b). Generated during training-best epoch.
See this image and copyright information in PMC

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