LiteFANet: A lightweight UNet-based fusion-attention segmentation network for 2D and 3D medical images

Current state-of-the-art 2D and 3D medical image-segmentation methods have achieved remarkable accuracy, yet they usually incur high computational overhead and large model sizes, making deployment on edge devices challenging. To address this issue, we propose LiteFANet, a new lightweight medical image-segmentation model that compresses parameters and reduces computational complexity without noticeably sacrificing segmentation accuracy. Built upon a simplified U-Net backbone, LiteFANet introduces a lightweight multi-branch feature-fusion module for more efficient integration of local and global information. In addition, we design a multi-semantic spatial–channel collaborative attention module that preserves long-range dependency modeling while substantially cutting the computational burden of self-attention. Experiments demonstrate that, even with parameter counts kept below 0.92 M (2D) and 0.59 M (3D), LiteFANet attains outstanding performance on three 2D and 3D benchmark medical segmentation datasets, confirming an excellent trade-off between accuracy and efficiency. Our method is highly practical, and the code can be found at https://github.com/CR818-web/LiteFANet.