ArticleScientific reports2025
A dual encoder network with multiscale feature fusion and multiple pooling channel spatial attention for skin scar image segmentation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AttenUNeT X with iterative feedback mechanisms for robust deep learning skin lesion segmentation.Scientific reports · 2025Article
- A dual attention and cross layer fusion network with a hybrid CNN and transformer architecture for medical image segmentation.Scientific reports · 2025Article
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
Skin scar is a prevalent dermatological concern that impacts both aesthetic appearance and psychological well-being, making precise delineation of scar tissue essential for clinical treatment. To address the challenge of scar image segmentation, this study introduces an innovative deep learning framework integrating CNN and Swin Transformer architectures. The proposed model leverages a multi-scale feature fusion module to combine hierarchical representations from both backbones, while a novel multi-pooling channel-spatial attention mechanism enhances feature refinement during skip connections. Comprehensive experiments demonstrate the model's superior performance in scar segmentation, achieving metrics of 96.01% Accuracy, 77.43% Precision, 90.17% Recall, 71.38% Jaccard Index, and 83.21% Dice Coefficient, which compare favorably with mainstream methods, and our model performs well in all metrics, highlighting its potential for clinical adoption in scar analysis.
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Registered trials
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