ArticleJournal of imaging informatics in medicine2024
Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Verifiable Framework for Brain Tumor Classification: Combining Vision Transformers, Class-Weighted Learning, and SMT-Based Formal Decision Traces.Diagnostics (Basel, Switzerland) · 2026Article
- From local textures to global attention: a hybrid feature fusion network for skin lesion classification.Scientific reports · 2026Article
- LGGC-Net: a local-global graph and color attention-based lightweight CNN for skin cancer classification.Scientific reports · 2026Article
- Deep Learning-Assisted Early Detection of Skin Cancer from Dermoscopic Images in Underserved Clinical Settings.Bioengineering (Basel, Switzerland) · 2026Article
- A hybrid approach for accurate skin lesion segmentation using LEDNet and Swin-UMamba.Scientific reports · 2026Article
- Transformer-Based Foundation Learning for Robust and Data-Efficient Skin Disease Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable Deep Learning Framework for Reliable Species-Level Classification Within the GeneraBiology · 2026Article
- Automated transperineal ultrasound analysis using deep learning for pelvic floor dysfunction assessment after total hysterectomy.Frontiers in oncology · 2026Article
- A survey of transformer-based architectures in medical image analysis: models, applications, and challenges.Frontiers in artificial intelligence · 2026Review
- Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy.Journal of medical signals and sensors · 2026Article
- Advancing skin cancer diagnosis with deep learning and attention mechanisms.Scientific reports · 2025Article
- Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems.Scientific reports · 2025Article
- A robust deep learning framework for multiclass skin cancer classification.Scientific reports · 2025Article
- Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.Scientific reports · 2025Article
- Advances in intelligent recognition and diagnosis of skin scar images: concepts, methods, challenges, and future trends.Frontiers in medicine · 2025Review
- A lightweight deep learning method to identify different types of cervical cancer.Scientific reports · 2024Article
- Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.Scientific reports · 2024Article
- Application of improved Unet network in the recognition and segmentation of lung CT images in patients with pneumoconiosis.BMC medical imaging · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning-based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature.
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