Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

Researchers developed an interpretable deep learning framework using EfficientNet-B0 and attention mechanisms to classify sperm morphology for male infertility diagnosis. The model achieves 90-94% accuracy on public datasets while providing visual explanations through Grad-CAM++ visualizations, addressing the clinical adoption barrier of traditional black-box AI models.

This research addresses a critical gap in medical AI adoption: the interpretability problem. While deep learning excels at pattern recognition, healthcare professionals require transparency to validate and trust algorithmic decisions. The proposed framework combines architectural efficiency with explainability, making it viable for real clinical settings where accountability matters. The integration of CBAM (Convolutional Block Attention Module) with EfficientNet-B0 enables the model to focus computational resources on diagnostically relevant sperm morphology features, improving both performance and interpretability simultaneously.

The medical diagnostics field has progressively recognized that accuracy alone is insufficient for deployment. Regulatory bodies and clinicians demand understanding of how AI reaches conclusions, particularly in fertility assessment where results directly impact patient treatment decisions. This study's use of Grad-CAM++ visualizations directly supports this requirement by revealing which sperm morphological features drive classification decisions.

The results demonstrate competitive performance against baseline models while maintaining clinical transparency. Testing on two public datasets (SMIDS and HuSHem) with macro F1 scores exceeding 0.91 suggests the approach generalizes beyond single-institution data. For medical device manufacturers and fertility clinics, interpretable AI frameworks reduce regulatory friction and accelerate adoption in clinical workflows.

Looking forward, similar attention-guided architectures could establish a new standard for medical image analysis across other diagnostic domains. The success here validates that interpretability and accuracy need not be trade-offs, potentially influencing how healthcare AI develops over the next 5-10 years. Developers should monitor whether regulatory frameworks begin preferring or mandating explainability in approved medical AI systems.

• →Attention-guided deep learning framework achieves 90-94% accuracy in sperm morphology classification while maintaining interpretability through Grad-CAM++ visualizations.
• →Combining EfficientNet-B0 with CBAM enables the model to focus on clinically relevant sperm features, improving both performance and explainability.
• →Results outperform standard EfficientNet-B0 and SimpleCNN baselines on two public datasets, demonstrating generalization capability.
• →Interpretable AI addresses a major barrier to clinical adoption of medical AI systems by providing transparent decision-making mechanisms.
• →The framework represents a practical tool ready for deployment in fertility clinics, bridging the gap between AI accuracy and clinical usability requirements.