Design and Implementation of a Deep Learning System to Analyze Bovine Sperm Morphology

Area of Science:

• Veterinary Science
• Animal Reproduction
• Biotechnology

Background:

• Sperm morphology analysis is crucial for evaluating bull fertility and detecting reproductive issues.
• Traditional methods are labor-intensive, subjective, and prone to errors, necessitating automated solutions.

Purpose of the Study:

• To design and implement a computer-aided system for objective bovine sperm morphology analysis.
• To leverage deep learning for automated detection and classification of sperm defects.

Main Methods:

• A sequential deep learning framework utilizing the YOLOv7 object detection model was developed.
• The system was trained on 277 annotated bull sperm micrographs across six morphological categories.
• The model segments and analyzes individual sperm cells, identifying defects in various parts.

Main Results:

• The system achieved a global mean Average Precision at 50% IoU (mAP@50) of 0.73.
• Demonstrated precision of 0.75 and recall of 0.71, indicating effective detection and classification.
• The deep learning approach offers a balanced trade-off between accuracy and computational efficiency.

Conclusions:

• The developed AI system significantly enhances efficiency and accuracy in bovine sperm quality assessment.
• This automated solution reduces reliance on manual analysis, benefiting veterinary reproduction laboratories.
• It provides a cost-effective and scalable method for improving bull fertility evaluations.