ArticleComputer methods and programs in biomedicine2021
Faster region convolutional neural network and semen tracking algorithm for sperm analysis.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.Journal of medical systems · 2023Pooled it
- An Artificial Intelligence-Based Computer Vision Model for Human Sperm Concentration, Motility, and Kinematics Analysis.Smart medicine · 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
- Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development.Reproductive biology and endocrinology : RB&E · 2025Observational
- [Advances in the Application of Artificial Intelligence in the Field of Male Infertility].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Review
- Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.Current urology reports · 2025Review
- Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis.Medicina (Kaunas, Lithuania) · 2024Review
- Sperm motility assessed by deep convolutional neural networks into WHO categories.Scientific reports · 2023Article
- YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning for sperm selection.Nature reviews. Urology · 2021Review
- A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure.Reproductive medicine and biologyArticle
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesSemen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm is a challenging task for researchers.
methodsThe paper proposes a novel Faster Region Convolutional Neural Network (FRCNN) with Elliptic Scanning Algorithm (ESA) for classifying human sperm and a Novel Tail to Head movement algorithm (THMA) for the motility analysis and tracking. This proposed method improves the accuracy of computer assisted semen analysis (CASA).
resultsThe proposed method outperforms and provides better results than existing methods. Method provides better accuracy of 97.37%. Sperm detection and identifying the sperm motility in the group is performed with minimum execution time of 1.12 s.
conclusionsA novel FRCNN with ESA detection algorithm is proposed for the analysis of human sperm classification. This method provides an accuracy of 97.37%. A Tail head movement-based (THMA) algorithm is explained for the motility analysis.
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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.