Sperm detection and tracking methods from this paper: https://arxiv.org/abs/2002.04034

[TASE 2024] SFCNet: Automated Non-Invasive Analysis of Motile Sperms Using Sperm Feature-Correlated Network

Classification of sperm heads based on its morphological quality via Mask-RCNN

Trained YOLOv8 model on Pose estimation with sperm cells.

Automated sperm motility analysis pipeline with OpenCV-based detection and standard motility parameter calculations (VCL, VSL, VAP). Features motion-aware cascade matching and batch processing