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Deep learning for the classification of human sperm.

Jason Riordon1, Christopher McCallum1, David Sinton1

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON, M5S 3G8, Canada.

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Summary
This summary is machine-generated.

This study introduces a deep learning method for automated sperm classification, improving accuracy and efficiency in semen analysis. This artificial intelligence approach has the potential to surpass human expert performance in evaluating sperm morphology.

Keywords:
Convolutional neural networkDeep learningFertilitySperm diagnosticsSperm head classificationSperm selectionTransfer learning

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Image Analysis

Background:

  • Infertility affects couples globally, necessitating advanced reproductive assistance.
  • Semen analysis, particularly sperm morphology evaluation, is crucial for assessing male fertility.
  • Automated sperm classification using machine learning offers potential for standardization and efficiency.

Purpose of the Study:

  • To develop and evaluate a deep learning method for classifying sperm morphology.
  • To automate, standardize, and expedite the process of semen analysis.

Main Methods:

  • Utilized VGG16, a deep convolutional neural network (CNN), for sperm head classification.
  • Retrained the VGG16 model using two publicly available sperm head datasets (HuSHeM and SCIAN).

Main Results:

  • Achieved high accuracy in classifying sperm morphology.
  • Demonstrated superior true positive rates compared to cascade ensemble of support vector machines (CE-SVM).
  • Exhibited comparable true positive rates to adaptive patch-based dictionary learning (APDL) methods.

Conclusions:

  • The deep learning approach provides a viable method for automating and accelerating semen analysis.
  • Artificial intelligence in sperm classification shows potential to exceed human expert accuracy, reliability, and throughput.