Multiscale Feature Fusion Booster Network for Segmentation of Colorectal Polyp

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: March 1, 2025

ABSTRACT Addressing the challenges posed by colorectal polyp variability and imaging inconsistencies in endoscopic images, we propose multiscale feature fusion booster network (MFFB‐Net), a novel deep learning (DL) framework for semantic segmentation of polyps to aid early cancer detection. Unlike prior models, such as pyramid vision transformer‐based cascaded attention decoder (PVT‐CASCADE) parallel reverse (PraNet), MFFB‐Net enhances accuracy efficiency through unique extraction both encoder stages, coupled with module refining fine‐grained details bottleneck efficient compression. The leverages multipath skip connections, capturing local global contextual information, is rigorously evaluated on seven benchmark datasets, including Kvasir, CVC‐ClinicDB, CVC‐ColonDB, ETIS, CVC‐300, BKAI‐IGH, EndoCV2020. achieves state‐of‐the‐art (SOTA) performance, Dice scores 94.38%, 91.92%, 91.21%, 80.34%, 82.67%, 76.92%, 74.29% EndoCV2020, respectively, outperforming existing models computational efficiency. real‐time processing speeds 26 FPS only 1.41 million parameters, making it well suited real‐world clinical applications. results underscore robustness MFFB‐Net, demonstrating its potential deployment computer‐aided diagnosis systems setting new automated segmentation.

Language: Английский

Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(4), P. 92 - 92

Published: March 21, 2025

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse morphologies, ambiguous boundaries make this task difficult. To address these issues, we propose Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: Hybrid Filtering (MGHF) module extracting multi-scale contextual information, Dynamic Granularity Partition Synergy (DGPS) enhancing polyp-background differentiation through adaptive feature interaction, (MGRA) mechanism further optimizing boundary recognition. Extensive experiments on ColonDB CVC-300 datasets demonstrate that S-MGRAUNet significantly outperforms existing methods while achieving competitive results Kvasir-SEG ClinicDB datasets, validating its accuracy, robustness, generalization capability, all effectively reducing computational complexity. This study highlights value multi-granularity extraction attention mechanisms, providing new insights practical guidance advancing theories medical image segmentation.

Language: Английский

LeXNet++: Layer-wise eXplainable ResUNet++ framework for segmentation of colorectal polyp cancer images

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Language: Английский