ArticleBMC medical imaging2024
Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Prediction of infiltration degree of ground-glass nodules using a fusion of CT radiomics and deep learning.Scientific reports · 2026Article
- Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.Scientific reports · 2026Article
- Lightweight hybrid foundation model for lung cancer prognosis based on low-dose chest X-ray images.Translational lung cancer research · 2026Article
- Hybrid CNN-transformer model with BM3D and YOLOv8 for early detection of lung cancer in low-dose CT scans.Scientific reports · 2026Article
- A Trustable Spine Abnormalities Classification System Using ResNet50 and VGG16 Supported by Explainable Artificial Intelligence.Biomimetics (Basel, Switzerland) · 2026Article
- Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.BMC medical research methodology · 2026Review
- Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection.Scientific reports · 2026Article
- A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection.Scientific reports · 2026Article
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
- LungCraft: a hybrid 3D-2D deep learning and radiomics framework with explainable AI for precision diagnosis of lung cancer.Frontiers in artificial intelligence · 2026Article
- Symmetry-guided explainable deep learning for colon cancer diagnosis: model benchmarking, cross-validation, statistical analysis, and explainability via ablation studies.Frontiers in artificial intelligence · 2026Article
- Predicting pathological complete response to breast cancer neoadjuvant therapy using multi-combination machine learning models based on vision transformer features.Scientific reports · 2025Article
- Dynamic-Attentive Pooling Networks: A Hybrid Lightweight Deep Model for Lung Cancer Classification.Journal of imaging · 2025Article
- Lung adenocarcinoma subtype classification based on contrastive learning model with multimodal integration.Scientific reports · 2025Article
- Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients.Future oncology (London, England) · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping.Frontiers in medicine · 2025Article
- Cerebrovascular diagnosis using CTA-based intracranial aneurysm classification via transfer learning and Grad-CAM visualization.Frontiers in neurologyArticle
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models-VGG16, ResNet50, and InceptionV3-combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.