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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957213/ https://www.ncbi.nlm.nih.gov/pubmed/36833377 http://dx.doi.org/10.3390/genes14020451 |
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author | Dobrovolny, Michal Benes, Jakub Langer, Jaroslav Krejcar, Ondrej Selamat, Ali |
author_facet | Dobrovolny, Michal Benes, Jakub Langer, Jaroslav Krejcar, Ondrej Selamat, Ali |
author_sort | Dobrovolny, Michal |
collection | PubMed |
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: see text]. |
format | Online Article Text |
id | pubmed-9957213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99572132023-02-25 Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset Dobrovolny, Michal Benes, Jakub Langer, Jaroslav Krejcar, Ondrej Selamat, Ali Genes (Basel) Article 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: see text]. MDPI 2023-02-09 /pmc/articles/PMC9957213/ /pubmed/36833377 http://dx.doi.org/10.3390/genes14020451 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dobrovolny, Michal Benes, Jakub Langer, Jaroslav Krejcar, Ondrej Selamat, Ali Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset |
title | 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_short | Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset |
title_sort | study on sperm-cell detection using yolov5 architecture with labaled dataset |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957213/ https://www.ncbi.nlm.nih.gov/pubmed/36833377 http://dx.doi.org/10.3390/genes14020451 |
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