Performance Optimization of U-Net Network in Segmentation of Nerves Image Dataset

This research explores the performance and optimization of U-Net architectures for segmenting nerve images. U-Nets are prevalent in medical image analysis due to their effectiveness in capturing detailed features and spatial relationships. Our focus is on enhancing U-Net models by fine-tuning hyperparameters, applying data augmentation techniques, and utilizing advanced loss functions to boost segmentation accuracy and computational efficiency. The study compares various U-Net variants, evaluating their effectiveness in preserving the delicate structures of nerve tissues. The results indicate that optimized U-Nets deliver high accuracy with reduced processing times, making them ideal for real-time medical applications. This research provides valuable insights into the practical use of U-Nets for nerve image segmentation and suggests avenues for future improvements.