Filter-Based Preprocessing Neural Network Model for Microorganism Detection Improvement
摘要
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method effectively reduces artifacts—such as blurred boundaries and unclear edges—that commonly hinder precise detection in live, unstained samples. The results demonstrate a marked improvement in detection quality over conventional approaches, highlighting the potential of our model to refine and elevate microorganism detection standards in both biomedical and industrial applications.