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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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A Dual Architecture Fusion and AutoEncoder for Automatic Morphological Classification of Human Sperm.

Muhammad Izzuddin Mahali1,2, Jenq-Shiou Leu1, Jeremie Theddy Darmawan1,3

  • 1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan.

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PubMed
Summary

This study introduces SwinMobile-AE, a novel deep learning model for accurate sperm classification, significantly improving assisted reproduction success rates. The AI-driven approach enhances accuracy and speed in analyzing sperm morphology.

Keywords:
deep learningdual architecture fusionmorphological classificationspermswin transformer

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Reproductive Medicine and Urology

Background:

  • Infertility affects global health, necessitating medical assistance for reproduction.
  • Accurate sperm classification is crucial for the success of assisted reproductive techniques.
  • Existing methods for sperm analysis lack the desired accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate sperm classification.
  • To improve the efficiency and reliability of sperm analysis in clinical settings.
  • To compare the performance of the proposed model against state-of-the-art methods.

Main Methods:

  • Development of a deep learning fusion architecture, SwinMobile, combining Shifted Windows Vision Transformer (Swin) and MobileNetV3.
  • Incorporation of an autoencoder (AE) for noise reduction within the SwinMobile-AE model.
  • Validation of the model on three diverse datasets: SVIA, HuSHem, and SMIDS.

Main Results:

  • The SwinMobile-AE model demonstrated strong sperm classification capabilities across all three datasets.
  • Achieved superior performance compared to state-of-the-art models, with accuracy benchmarks including 95.4% (SVIA), 97.6% (HuSHem), and 91.7% (SMIDS).
  • The model exhibited high accuracy, reliability, and speed, rivaling human analytical capabilities.

Conclusions:

  • The proposed SwinMobile-AE deep learning approach is suitable for modernizing sperm classification in clinical practice.
  • This AI-driven method offers technological advancements for classifying sperm morphology with high precision.
  • The model's robust performance on varied datasets highlights its potential for widespread clinical adoption.