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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020215" target="_blank" >RIV/62690094:18450/23:50020215 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2073-4425/14/2/451" target="_blank" >https://www.mdpi.com/2073-4425/14/2/451</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/genes14020451" target="_blank" >10.3390/genes14020451</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.). © 2023 by the authors.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.). © 2023 by the authors.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Genes

ISSN

2073-4425

e-ISSN

2073-4425

Svazek periodika

14

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

"Article number: 451"

Kód UT WoS článku

000945699200001

EID výsledku v databázi Scopus

2-s2.0-85148882863