| Sumario: | This study introduces ColonNeXt, a novel fully convolutional attention-based model for polyp segmentation from colonoscopy images, aimed at the enhancing early detection of colorectal cancer. Utilizing a purely convolutional neural network (CNN), ColonNeXt integrates an encoder-decoder structure with a hierarchical multi-scale context-aware network (MSCAN) in the encoder and a convolutional block attention module (CBAM) in the decoder. The decoder further includes a proposed CNN-based feature attention mechanism for selective feature enhancement, ensuring precise segmentation. A new refinement module effectively improves boundary accuracy, addressing challenges such as variable polyp size, complex textures, and inconsistent illumination. Evaluations on standard datasets show that ColonNeXt achieves high accuracy and efficiency, significantly outperforming competing methods. These results confirm its robustness and precision, establishing ColonNeXt as a state-of-the-art model for polyp segmentation. The code is available at: https://github.com/long-nguyen12/colonnext-pytorch.
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