首页|MCGFF-Net: a multi-scale context-aware and global feature fusion network for enhanced polyp and skin lesion segmentation
MCGFF-Net: a multi-scale context-aware and global feature fusion network for enhanced polyp and skin lesion segmentation
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NETL
NSTL
Springer Nature
Abstract Accurate segmentation of polyps and skin lesions is crucial for clinical diagnosis. While many UNet-based methods have made significant progress, challenges such as variations in the scale of segmentation targets and difficulty distinguishing lesion regions from normal tissues persist. To address this, we propose an efficient multi-scale context-aware and global feature fusion network called MCGFF-Net, which aims to overcome these challenges. Specifically, we incorporated the CBAM attention module into the encoder to enhance the ability to capture key information. We further propose a multi-scale perception module that adaptively extracts multiscale semantic information to address the challenge of scale variation in lesion regions. To enhance the interaction of semantic information between cross-layer features, we design a cross-layer feature fusion module (CFM) to alleviate the problem of some lesion regions not being clearly distinguished from the background. Additionally, an efficient pyramid channel attention module is included in the CFM to filter noise and redundant information. We conduct extensive experiments on five publicly available skin lesion and polyp datasets, including CVC-ClinicDB, Kvasir-SEG, ISIC2017, ISIC2018, and PH2. The results indicate that our MCGFF-Net outperforms current popular methods, achieving excellent performance in polyp and skin lesion segmentation tasks. The code is available at https://github.com/liyanxiang985/MCGFF-Net.
NETL
NSTL
Springer Nature
