Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification

Abstract

Male infertility remains a significant global health concern, with abnormal sperm head morphology recognized as a key factor impacting fertility. Traditional analysis of sperm morphology through manual microscopy is labor-intensive and susceptible to variability among observers. In this study, we introduce a deep learning framework designed to automate sperm head classification, integrating EdgeSAM for precise segmentation with a Sperm Head Pose Correction Network to standardize orientation and position. The classification network employs flip feature fusion and deformable convolutions to capture symmetrical characteristics, which enhances classification accuracy across morphological variations. Our model achieves a test accuracy of 97.5% on the HuSHem and Chenwy datasets, outperforming existing methods and demonstrating greater robustness to rotational and translational transformations. This approach offers a streamlined, automated solution for sperm morphology analysis, providing a reliable tool to support clinical fertility diagnostics and research applications.