An In-Depth Analysis of U-Net Variations for Advancing Precision in Medical Image Segmentation

In both diagnosis and therapy planning, medical image segmentation is vital. The implementation of different U-Net variants for semantic segmentation is examined in this paper. Dense U-Net aims to improve feature extraction and propagation by establishing dense connections between the encoder and decoder circuits. Attention U-Net employs attention mechanisms to enhance segmentation accuracy by highlighting significant elements in complex and cluttered scenes. Additionally, we suggest a Hybrid model that combines Attention U-Net and Dense U-Net elements. The goal of this hybridization is to provide a more flexible and all-encompassing framework for image segmentation applications by fusing the benefits of both architectures. Combining these variations is intended to improve semantic segmentation models accuracy and stability, especially when dealing with complex structures or a lack of annotated data. This study compares these three U-Net models and the findings show that the accuracy and precision of hybrid U-Net outperforms the individual U-Net models.