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<p>Colorectal polyps are precancerous lesions with a high risk of developing into cancer if left untreated. Colonoscopy is the gold standard for detecting and removing these polyps, but it has high miss rates, especially for small and flat polyps. Deep learning methods have been increasingly used to aid in polyp detection, but their performance remains limited when applied to samples captured under unconstrained conditions or when processing images of small and flat polyps. To address this challenge, we propose PolyNeXt, a novel polyp segmentation network that leverages ConvNeXtV2 and global response normalization (GRN) layers. We train PolyNeXt on a combined dataset of samples from Kvasir-SEG and CVC-ClinicDB and evaluate it on four public distinct datasets: ETIS-LaribPolypDB, CVC-ColonDB, CVC-300, and BKAI-IGH NeoPolyp-Small. PolyNeXt achieves state-of-the-art performance on polyps captured in suboptimal conditions, outperforming other methods in terms of Intersection over Union (IoU) and Dice coefficient. Our work demonstrates that PolyNeXt is an effective polyp segmentation network that can improve the accuracy and reliability of detecting polyps captured under challenging circumstances.</p>

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PolyNeXt: a novel semantic segmentation network for polyp detection in colonoscopy images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

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  • Khaled ELKarazle,
  • Valliappan Raman,
  • Caslon Chua,
  • Patrick Then

摘要

Colorectal polyps are precancerous lesions with a high risk of developing into cancer if left untreated. Colonoscopy is the gold standard for detecting and removing these polyps, but it has high miss rates, especially for small and flat polyps. Deep learning methods have been increasingly used to aid in polyp detection, but their performance remains limited when applied to samples captured under unconstrained conditions or when processing images of small and flat polyps. To address this challenge, we propose PolyNeXt, a novel polyp segmentation network that leverages ConvNeXtV2 and global response normalization (GRN) layers. We train PolyNeXt on a combined dataset of samples from Kvasir-SEG and CVC-ClinicDB and evaluate it on four public distinct datasets: ETIS-LaribPolypDB, CVC-ColonDB, CVC-300, and BKAI-IGH NeoPolyp-Small. PolyNeXt achieves state-of-the-art performance on polyps captured in suboptimal conditions, outperforming other methods in terms of Intersection over Union (IoU) and Dice coefficient. Our work demonstrates that PolyNeXt is an effective polyp segmentation network that can improve the accuracy and reliability of detecting polyps captured under challenging circumstances.