ArticleScientific reports2026
Multi-scale and edge-aware IFGNet for precise skin lesion segmentation in high-resolution dermoscopic images.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate segmentation of dermoscopic images is essential for early melanoma diagnosis, yet current methods remain limited. CNN-based models capture local details but lack global context, whereas Transformer-based approaches model long-range dependencies but often lose fine structures; both struggle with blurred boundaries and scale variations. To address these challenges, we propose IFGNet, a hybrid segmentation framework that integrates CNN-Transformer synergy, multi-scale convolution, and boundary-aware decoding. Specifically, our design combines local-global feature fusion, large-kernel parallel convolutions without dilation, and a boundary refinement strategy to enhance lesion consistency. Extensive experiments on the ISIC 2016, ISIC 2017, and ISIC 2018 benchmarks demonstrate that IFGNet consistently surpasses state-of-the-art methods in Dice and IoU. These results highlight the effectiveness of IFGNet for accurate high-resolution skin lesion segmentation, and suggest its potential for clinical computer-aided melanoma diagnosis.
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