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Deep learning framework enhances sperm morphology classification with improved interpretability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an attention-guided deep learning framework to improve the interpretability and accuracy of sperm morphology classification. By integrating a pre-trained EfficientNet-B0 model with a Convolutional Block Attention Module (CBAM), the system effectively focuses on critical sperm head features. This approach achieved high accuracy rates of 90.2% and 93.9% on public datasets, surpassing simpler models and providing visual explanations for its classifications. AI

IMPACT This research offers a more transparent and accurate AI tool for clinical applications in fertility analysis.

RANK_REASON The cluster contains an academic paper detailing a new deep learning framework for a specific classification task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning framework enhances sperm morphology classification with improved interpretability

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zahra Asghari Varzaneh, Reza Khoshkangini, Thomas Ebner, Lars Johansson ·

    Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

    arXiv:2606.20438v1 Announce Type: new Abstract: Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology. While deep learning models offer automated analysis, most lack interpretability, limiting their clinical adoption. This study propos…

  2. arXiv cs.AI TIER_1 English(EN) · Lars Johansson ·

    Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

    Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology. While deep learning models offer automated analysis, most lack interpretability, limiting their clinical adoption. This study proposes an attention-guided deep learning framework f…

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