ArticleScientific reports2025
Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- Class-balanced dermoscopic lesion segmentation using MoG-LISA and optimized Swin-UNet via the GM-FDE framework.iScience · 2026Article
- An intelligent method for Buoy meteorological data restoration using a Spatio-Temporal Dual-Attention Network with transformer and GAT.PloS one · 2026Article
- AttenUNeT X with iterative feedback mechanisms for robust deep learning skin lesion segmentation.Scientific reports · 2025Article
- Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.Abdominal radiology (New York) · 2025Article
- H-fusion SEG: dual-branch hyper-attention fusion network with SAM integration for robust skin disease segmentation.Scientific reports · 2025Article
- Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters.Scientific reports · 2025Article
- Brain tumor classification using GAN-augmented data with autoencoders and Swin Transformers.Frontiers in medicine · 2025Article
- An explainable hybrid deep learning framework for precise skin lesion segmentation and multi-class classification.Frontiers in medicine · 2025Article
- FocusGate-Net: A dual-attention guided MLP-convolution hybrid network for accurate and efficient medical image segmentation.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg's effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.
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