Unveiling the capabilities of vision transformers in sperm morphology analysis: a comparative evaluation

Area of Science:

• Biomedical Image Analysis
• Artificial Intelligence in Medicine
• Reproductive Medicine

Background:

• Traditional sperm morphology analysis is manual and labor-intensive.
• Current automated methods, including CNNs, face challenges in accuracy and pre-processing.
• Sperm morphology analysis is crucial for male fertility assessment.

Purpose of the Study:

• To develop a novel, end-to-end automated sperm morphology analysis framework using vision transformers (ViTs).
• To evaluate ViT performance across different variants and hyperparameter settings.
• To compare ViT-based methods against traditional and CNN-based approaches.

Main Methods:

• Implemented a vision transformer (ViT) framework for direct processing of raw sperm images.
• Conducted extensive hyperparameter optimization for eight ViT variants.
• Utilized benchmark datasets (HuSHeM, SMIDS) and compared performance with CNNs and hybrid models.
• Employed visualization techniques like Attention Maps and Grad-CAM.

Main Results:

• Vision transformers significantly outperformed CNNs and traditional methods in accuracy.
• The BEiT_Base ViT model achieved state-of-the-art accuracies of 92.5% (SMIDS) and 93.52% (HuSHeM).
• Data augmentation was found to enhance ViT generalization, especially with limited data.

Conclusions:

• ViT-based frameworks provide a scalable, fully automated solution for sperm morphology analysis, reducing manual intervention.
• These models demonstrate superior ability in capturing complex morphological features.
• The study highlights the potential of transformer models in clinical andrology and biomedical imaging.