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Deep learning model enhances sperm morphology classification with attention guidance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an attention-guided deep learning framework to improve the interpretability and accuracy of sperm morphology classification. This model combines a pre-trained EfficientNet-B0 with a Convolutional Block Attention Module (CBAM) to pinpoint critical features of sperm heads. When tested on the SMIDS and HuSHem datasets, the framework achieved high macro F1 scores of 0.913 and 0.948, respectively, surpassing other models like SimpleCNN and standard EfficientNet-B0. The use of Grad-CAM++ visualizations further enhances transparency by highlighting the features that influence the model's diagnostic decisions, making it a practical tool for fertility clinics. AI

IMPACT This research offers a more transparent and accurate AI tool for clinical diagnostics in male infertility, potentially improving patient outcomes.

RANK_REASON The cluster contains an academic paper detailing a new deep learning model and its evaluation on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]

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

Deep learning model enhances sperm morphology classification with attention guidance

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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