Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916833773223936 |
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| author | Kanrar, Sayandeep Piyush, Raja Razi, Qaiser Chakraborty, Debanshi Hassija, Vikas Chalapathi, GSS |
| author_facet | Kanrar, Sayandeep Piyush, Raja Razi, Qaiser Chakraborty, Debanshi Hassija, Vikas Chalapathi, GSS |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+ Kanrar, Sayandeep Piyush, Raja Razi, Qaiser Chakraborty, Debanshi Hassija, Vikas Chalapathi, GSS Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+ |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.00511 |