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Study on sperm-cell detection using YOLOv5 architecture with labaled dataset

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 usi...

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Main Authors: Dobrovolny, Michal, Benes, Jakub, Langer, Jaroslav, Krejcar, Ondrej
Format: Article
Language:English
Published: MDPI 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/100498/1/AliSelamat2022_StudyonSpermCellDetectionUsingYOLOv5Architecture.pdf
http://eprints.utm.my/id/eprint/100498/
http://dx.doi.org/10.3390/genes14020451
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spelling my.utm.1004982023-04-14T02:16:42Z http://eprints.utm.my/id/eprint/100498/ Study on sperm-cell detection using YOLOv5 architecture with labaled dataset Dobrovolny, Michal Benes, Jakub Langer, Jaroslav Krejcar, Ondrej T Technology (General) 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 (Formula presented.). MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100498/1/AliSelamat2022_StudyonSpermCellDetectionUsingYOLOv5Architecture.pdf Dobrovolny, Michal and Benes, Jakub and Langer, Jaroslav and Krejcar, Ondrej (2022) Study on sperm-cell detection using YOLOv5 architecture with labaled dataset. Genes, 14 (2). pp. 1-14. ISSN 2073-4425 http://dx.doi.org/10.3390/genes14020451 DOI : 10.3390/genes14020451
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Dobrovolny, Michal
Benes, Jakub
Langer, Jaroslav
Krejcar, Ondrej
Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
description 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 (Formula presented.).
format Article
author Dobrovolny, Michal
Benes, Jakub
Langer, Jaroslav
Krejcar, Ondrej
author_facet Dobrovolny, Michal
Benes, Jakub
Langer, Jaroslav
Krejcar, Ondrej
author_sort Dobrovolny, Michal
title Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
title_short Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
title_full Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
title_fullStr Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
title_full_unstemmed Study on sperm-cell detection using YOLOv5 architecture with labaled dataset
title_sort study on sperm-cell detection using yolov5 architecture with labaled dataset
publisher MDPI
publishDate 2022
url http://eprints.utm.my/id/eprint/100498/1/AliSelamat2022_StudyonSpermCellDetectionUsingYOLOv5Architecture.pdf
http://eprints.utm.my/id/eprint/100498/
http://dx.doi.org/10.3390/genes14020451
_version_ 1764222575370567680
score 13.211869

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