Advancements in automated sperm morphology analysis: a deep learning approach with comprehensive classification and model evaluation

Abstract

Automated sperm morphology analysis is crucial in reproductive medicine for assessing male fertility, but existing methods often lack robustness in handling diverse morphological abnormalities across different regions of sperm. This study proposes a deep learning-based approach utilizing the ResNet50 architecture trained on a new SMD/MSS benchmarked dataset, which includes comprehensive annotations of 12 morphological defects across head, midpiece, and tail regions of sperm. Our approach achieved promising results with an accuracy of 95%, demonstrating effective classification across various sperm morphology classes. However, certain classes exhibited lower precision and recall rates, highlighting challenges in model performance for specific abnormalities. The findings underscore the potential of our proposed system in enhancing sperm morphology assessment. In fact, it is the first to comprehensively diagnose a spermatozoon by examining each part, including the head, intermediate piece, and tail, by identifying the type of anomaly in each part according to David's classification, which includes 12 different anomalies, to perform multi-label classification for a more precise diagnosis. It is unlike SOTA works which either study only the head or simply indicate whether each part of the sperm is normal or abnormal.