ReviewGenes2023
Study on Sperm-Cell Detection Using YOLOv5 Architecture with Labaled Dataset.
Review in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Deep Learning-Assisted Classification of Urinary Red Blood Cell Morphology for Glomerular Hematuria Screening: A Pilot Study.Journal of clinical laboratory analysis · 2026Article
- Spitz: a YOLO-based deep learning system for automated spermatozoa detection in forensic genetic analysis.International journal of legal medicine · 2026Article
- Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.Discover oncology · 2026Article
- An advanced YOLO-based image processing framework for automated sperm cell detection.Scientific reports · 2026Article
- A Novel Lightweight Deep Learning Model for Boar Sperm Head Detection in Microscopic Images: YOLO11_SRP.Animals : an open access journal from MDPI · 2026Article
- An artificial intelligence real-time rare sperm detection system for intraoperative microsurgical testicular sperm extraction.Human reproduction open · 2026Article
- Design and Implementation of a Deep Learning System to Analyze Bovine Sperm Morphology.Veterinary sciences · 2025Article
- AxiWorm: a new tool using YOLOv5 to test antiparasitic drugs against Trichinella spiralis.Parasites & vectors · 2025Article
- Multidimensional morphological analysis of live sperm based on multiple-target tracking.Computational and structural biotechnology journal · 2024Article
- Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.Reproductive biology and endocrinology : RB&E · 2024Article
- A modified U-Net to detect real sperms in videos of human sperm cell.Frontiers in artificial intelligence · 2024Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 72.15.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.