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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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Detalles Bibliográficos
Autores principales: Dobrovolny, Michal, Benes, Jakub, Langer, Jaroslav, Krejcar, Ondrej, Selamat, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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].
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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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