ArticleScientific reports2025
Explainable AI for lung cancer detection via a custom CNN on CT images.
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 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Responsible artificial intelligence in medical imaging: a systematic review.Frontiers in digital health · 2026Pooled it
- Innovative technologies and their clinical prospects for early lung cancer screening.Clinical and experimental medicine · 2025Pooled it
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- An Explainable Transformer-Based Framework for Lung Cancer Classification and Automated Radiology Report Generation from Multi-Slice CT Images.Biomedicines · 2026Article
- Multiclass lung cancer detection using a hybrid capsule inspired deep neural network.Scientific reports · 2026Article
- Improving wildlife track classification through human-in-the-loop method and explainable AI.Scientific reports · 2026Article
- Article
- Gene-based lung cancer detection system through omix data and optimized convolutional neural network.Journal of computer-aided molecular design · 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
- Multi-spatial channel attention and inceptionv3-based CAD system with optimized MLP for lung cancer detection.Journal of computer-aided molecular design · 2026Article
- Interpretable Diagnosis of Pulmonary Emphysema on Low-Dose CT Using ResNet Embeddings.Journal of imaging · 2026Article
- Frequency response compensation to optimize pretrained deep learning models in lung nodule malignancy classification.Frontiers in artificial intelligence · 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
- 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
- Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures.Frontiers in big data · 2026Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.Scientific reports · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment options and decreases survival rates. Early detection is essential for better outcomes, but traditional CT image analysis is time-consuming, prone to error, and relies on subjective judgments. To overcome these issues, we propose a custom convolutional neural network (CNN) combined with explainable AI (XAI) techniques, particularly gradient-weighted class activation mapping (Grad-CAM). This approach is intended to reliably classify lung cancer into squamous cell carcinoma, large cell carcinoma, or adenocarcinoma. Unlike conventional methods, our approach not only achieves highly accurate classification of lung cancer subtypes but also incorporates clinically validated interpretability features to ensure alignment with medical diagnostics. Our model trained on a comprehensive dataset of CT images achieved an overall accuracy of 93.06%. This performance demonstrates the model's robustness in detecting even subtle malignancies, with strong precision, recall, and F1-scores across all cancer types. Including interpretable Grad-CAM visualizations ensures reliability and transparency, aiding clinicians in understanding the model's predictions. This innovative method demonstrates the potential to revolutionize early lung cancer detection and improve patient survival rates by combining state-of-the-art accuracy with explainability tailored for clinical application.
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Registered trials
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