Optimizing Embryo Classification: Integrating Advanced Image Processing and Machine Learning Techniques for Enhanced Predictive Accuracy

Embryo classification is a critical step in assisted re productive technologies, enabling the selection of viable embryos for implantation and improving success rates. This research integrates convolutional neural networks (CNNs) with advanced image processing techniques to develop an automated and efficient embryo classification system. Data collection and preprocessing form the foundation of the study, involving the acquisition of high-quality embryo images and the application of normalization, augmentation, and standardization techniques to enhance data quality. Class imbalance is addressed using weighted training, and hyperparameters are optimized to achieve high performance. The system’s performance is evaluated through accuracy, precision, recall, and loss metrics, with visualizations like learning curves to monitor trends across epochs. Results demonstrate that CNN-based models can significantly improve classification accuracy compared to traditional methods. The findings underscore the potential of deep learning to revolutionize embryo selection by offering a scalable, accurate, and time-efficient solution. Future work aims to expand the dataset and refine the architecture for multi-class classification. This study contributes to the advance ment of machine learning applications in healthcare, particularly in enhancing decision-making in embryology.