Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

2026-06-18

AI Summary

This study addresses the limited interpretability and clinical deployment challenges of existing automated sperm morphology classification models by proposing an attention-guided deep learning framework. The approach employs a pretrained EfficientNet-B0 backbone augmented with Convolutional Block Attention Modules (CBAM) to emphasize diagnostically relevant regions of the sperm head, and integrates Grad-CAM++ for visual interpretability. Evaluated on the SMIDS and HuSHem datasets, the model achieves classification accuracies of 90.2% and 93.9%, with macro F1-scores of 0.913 and 0.948, respectively—significantly outperforming current baselines. By simultaneously enhancing both predictive performance and model transparency, this work advances the trustworthy integration of artificial intelligence into reproductive medicine.

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.

Problem

• sperm morphology
• male infertility
• deep learning
• interpretability
• clinical adoption

Innovation

• attention mechanism
• sperm morphology classification
• interpretable deep learning
• CBAM
• Grad-CAM++