Computer Assisted Analysis of Acrosomal Status in Sperm Cells Using Inverted Fluorescence Microscopy and Computational Intelligence for Biotechnology Applications

This study presents an automated deep learning approach for evaluating acrosomal membrane integrity in bovine spermatozoa, a critical parameter for assessing semen quality in artificial insemination. We implemented a convolutional neural network classifier based on the YOLOv5 architecture, employing transfer learning techniques. The model was trained on a dataset comprising 300 high-resolution fluorescence microscopy images (1280 x 1024 pixels), acquired using a Nikon Eclipse Ti2-U system. Samples were processed with dual fluorochrome staining (FITC- PNA and PI) to clearly distinguish between intact (intact acrosome, IA) and damaged (damaged acrosome, DA) acrosomal states. During model training, images were resized to 640 x 640 pixels and processed with a batch size of 8 over 80 epochs. The implemented system achieved classification accuracies of 97% for DA spermatozoa and 79% for AI spermatozoa. The lower accuracy for IA classification was primarily attributed to fluorescence decay during imaging and the relative scarcity of training data for this class. Comparative analysis showed significant advantages over manual evaluation, reducing processing time from several hours to minutes per sample. This automated approach provides laboratories with a cost-effective alternative to conventional Computer-Assisted Sperm Analysis (CASA), combining fluorescence microscopy accuracy with deep learning efficiency. Furthermore, its modular architecture allows for future expansion, potentially including motility, morphology, and viability parameters.