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Related Experiment Video

Updated: Jan 18, 2026

A Standardized Approach for Multispecies Purification of Mammalian Male Germ Cells by Mechanical Tissue Dissociation and Flow Cytometry
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Category-Aware Two-Stage Divide-and-Ensemble Framework for Sperm Morphology Classification.

Aydın Kağan Turkoglu1, Gorkem Serbes2, Hakkı Uzun3

  • 1Department of Computer Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, 34220 Istanbul, Turkey.

Diagnostics (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

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This summary is machine-generated.

A new two-stage deep learning framework automates sperm morphology classification, improving accuracy for male infertility diagnosis. This automated system reduces subjectivity and enhances reliability in assessing sperm health.

Area of Science:

  • Reproductive Biology
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Sperm morphology assessment is crucial for male infertility diagnosis but is subjective and operator-dependent.
  • Traditional manual methods lack accuracy due to biological variation and interpretation bias.
  • Automated systems are needed to improve objectivity and reliability in sperm analysis.

Purpose of the Study:

  • To develop a two-stage, fully automated framework for sperm morphology classification.
  • To reduce subjectivity and misclassification in identifying sperm abnormalities.
  • To enhance diagnostic support in male reproductive healthcare.

Main Methods:

  • A novel two-stage deep learning framework using a comprehensive 18-class dataset across three staining protocols.
Keywords:
NFNetdeep learningensemble learningsperm morphologytwo-stage classificationvision transformers

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Last Updated: Jan 18, 2026

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  • Stage 1: Categorization into head/neck abnormalities versus normal/tail abnormalities.
  • Stage 2: Ensemble model with NFNet-F4 and vision transformer (ViT) variants, using a structured multi-stage voting strategy.

    • Main Results:

    • The framework achieved accuracies of 69.43%, 71.34%, and 68.41% across staining protocols.
    • Demonstrated a statistically significant 4.38% improvement over existing literature approaches.
    • Substantially reduced misclassification among visually similar sperm abnormality categories.

    Conclusions:

    • The proposed two-stage, ensemble-based framework offers a robust solution for automated sperm morphology classification.
    • Hierarchical classification and structured decision fusion advance automated analysis beyond traditional methods.
    • Provides a reliable and scalable tool for clinical decision-making in male fertility assessment.