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

Traditional sperm morphology assessment relies on manual visual inspection or semi-automated computer-aided sperm analysis (CASA) systems, which often require labor-intensive pre-processing steps. While recent machine learning approaches, particularly convolutional neural networks (CNNs), have improved feature extraction from sperm images, achieving a fully automated and highly accurate system remains challenging due to the complexity of sperm morphology and the need for specialized image adjustments. This study presents a novel, end-to-end automated sperm morphology analysis framework based on vision transformers (ViTs), which processes raw sperm images from two benchmark datasets-Human Sperm Head Morphology (HuSHeM) and Sperm Morphology Image Data Set (SMIDS)-without manual pre-processing. We conducted an extensive hyperparameter optimization study across eight ViT variants, evaluating learning rates, optimization algorithms, and data augmentation scales. Our experiments demonstrated that data augmentation significantly enhances ViT performance by improving generalization, particularly in limited-data scenarios. A comparative analysis of CNNs, hybrid models, and pure ViTs revealed that transformer-based architectures consistently outperform traditional methods. The BEiT_Base model achieved state-of-the-art accuracies of 92.5% (SMIDS) and 93.52% (HuSHeM), surpassing prior CNN-based approaches by 1.63% and 1.42%, respectively. Statistical significance (