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Sperm-cell Detection Using YOLOv5 Architecture

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

Infertility has become a severe health issue in recent years. Sperm morphology, sperm motility, and sperm density are the most critical factors in male infertility. As a result, sperm motility, density, and morphology are examined in semen analysis carried out by laboratory professionals. However, applying a subjective analysis based on laboratory observation is easy to make a mistake. To reduce the effect of specialists in semen analysis, a computer-aided sperm count estimation approach is proposed in this work. The quantity of active sperm in the semen is determined using object detection methods focusing on sperm motility. The proposed strategy was tested using data from the Visem dataset provided by Association for Computing Machinery. We created a small sample custom dataset to prove that our network will be able to detect sperms in images. The best not-super tuned result is mAP 72.15.

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Acknowledgement

The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2022-2102).

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Michal Dobrovolny, Jakub Benes, Ondrej Krejcar & Ali Selamat

  2. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Malaysia

    Ali Selamat

Authors
  1. Michal Dobrovolny
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  2. Jakub Benes
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  3. Ondrej Krejcar
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  4. Ali Selamat
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Corresponding author

Correspondence to Michal Dobrovolny .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Dobrovolny, M., Benes, J., Krejcar, O., Selamat, A. (2022). Sperm-cell Detection Using YOLOv5 Architecture. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_27

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

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