Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+

TL;DR

This paper introduces VMSE-U-Net and VM-Unet CBAM+ architectures that incorporate attention mechanisms to significantly improve medical image segmentation accuracy, efficiency, and robustness across multiple datasets.

Contribution

The paper presents novel deep learning models with integrated attention modules, achieving superior segmentation performance and computational efficiency over existing VM-U-Net models.

Findings

VMSE-U-Net achieves highest accuracy, IoU, precision, and recall.

Models demonstrate faster inference and lower memory usage.

Enhanced architectures outperform baseline models on multiple datasets.

Abstract

In this paper, we present the VMSE U-Net and VM-Unet CBAM+ model, two cutting-edge deep learning architectures designed to enhance medical image segmentation. Our approach integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) techniques into the traditional VM U-Net framework, significantly improving segmentation accuracy, feature localization, and computational efficiency. Both models show superior performance compared to the baseline VM-Unet across multiple datasets. Notably, VMSEUnet achieves the highest accuracy, IoU, precision, and recall while maintaining low loss values. It also exhibits exceptional computational efficiency with faster inference times and lower memory usage on both GPU and CPU. Overall, the study suggests that the enhanced architecture VMSE-Unet is a valuable tool for medical image analysis. These findings highlight its potential for real-world clinical applications, emphasizing the importance of further research to optimize accuracy, robustness, and computational efficiency.