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 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 mod
The increasing maturity of deep learning coupled with growing awareness of male infertility as a global health issue drives demand for more advanced diagnostic tools.
This development enhances the clinical utility of AI in medical diagnostics by addressing a critical barrier to adoption: interpretability.
AI models for medical imaging, particularly in fertility, will become more transparent and trustworthy, paving the way for broader clinical integration and improved diagnostic accuracy.
- · Fertility clinics
- · Patients seeking infertility treatment
- · Healthcare AI developers
- · Medical diagnostic companies
- · Traditional manual sperm analysis methods
- · Companies relying on non-interpretable AI solutions
Automated and more accurate diagnoses for male infertility become standard practice.
Reduced burden on embryologists and clinicians, allowing for focus on treatment and patient care.
Enhanced AI interpretability frameworks could accelerate adoption across other sensitive medical diagnostic fields.
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Read at arXiv cs.AI