ArticleBMC medical imaging2024
VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- HRKHu-Loss: harmonic regularization Kullback Huber loss-based lung cancer detection and severity level classification using CT image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Swallow Winged Kite Optimization with Shuffle Attention Xtreme Gradient Boost Network for lung cancer detection using CT image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection.Scientific reports · 2026Article
- Explainable active reinforcement deep learning improves lung cancer detection from CT images.Scientific reports · 2026Article
- WISeRKNet: wide slice residual Kronecker network for lung cancer detection based on CT images.Scientific reports · 2026Article
- Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures.Frontiers in big data · 2026Article
- Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data.PloS one · 2026Article
- MLHNet-Lung: an attention-guided multi-level CNN-transformer fusion framework with CBAM and GeM pooling for explainable multiclass lung CT image classification.Frontiers in medicine · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set.Scientific reports · 2025Article
- Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.Scientific reports · 2025Article
- Explainable AI for lung cancer detection via a custom CNN on CT images.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.
methodsIn this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images.
resultsThe experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy.
conclusionVER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.
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