Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

Zahra Asghari Varzaneh, Reza Khoshkangini, Thomas Ebner, Lars Johansson

Keywords: Sperm Morphology, EfficientNet, Convolutional Block Attention, Grad-CAM++

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 proposes an attention-guided deep learning framework for sperm morphology classification. We combine a pretrained
EfficientNet-B0 with a Convolutional Block Attention Module (CBAM) to focus on key areas
of the sperm head, improving both accuracy and
interpretability. Evaluated on the SMIDS and
HuSHem public datasets, our model achieves accuracies of 90.2% and 93.9% (macro F1-scores
of 0.913 and 0.948), outperforming SimpleCNN
and standard EfficientNet-B0. Furthermore, we
use Grad-CAM++ visualizations to highlight features influencing the model’s decisions. The results demonstrate that this accurate and transparent framework is a practical tool for automated
sperm analysis in fertility clinics.

Track: Track 2: ML Research by Muslim Authors

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Submission Number: 56