ArticleScientific reports2022
Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 143 citations in OpenAlex.
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- RNNet-MST: A ResNet-50 with Multi-Scale Transformer Blocks for Pulmonary Nodule Classification and Attention-Based Localization on Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model.Scientific reports · 2026Article
- Hybrid CNN-transformer model with BM3D and YOLOv8 for early detection of lung cancer in low-dose CT scans.Scientific reports · 2026Article
- A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.Scientific reports · 2026Article
- A Symmetric Encoder-Decoder Network with Enhanced Group-Shuffle Modules for Robust Lung Nodule Detection in CT Scans.Biomimetics (Basel, Switzerland) · 2026Article
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.Scientific reports · 2025Article
- The impact of miRNAs on epithelial-mesenchymal transition in lung cancer and the latest advances in their use as diagnostic markers.Journal of cancer research and clinical oncology · 2025Review
- Enhanced Lung Cancer Classification Accuracy via Hybrid Sensor Integration and Optimized Fuzzy Logic-Based Electronic Nose.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research.Japanese journal of radiology · 2025Review
- Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.Physical and engineering sciences in medicine · 2025Article
- Deep learning-based lung cancer risk assessment using chest computed tomography images without pulmonary nodules ≥8 mm.Translational lung cancer research · 2025Article
- The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers.Journal of translational medicine · 2025Article
- LuGSAM: a novel framework for integrating text prompts to Segment Anything Model (SAM) for segmentation tasks of ICU chest x-rays.Multimedia tools and applications · 2025Article
- Educational insights of artificial intelligence assisted chest radiograph interpretation in lung cancer detection-pictorial review.BJR artificial intelligence · 2025Review
- Artificial Intelligence in Cancer Oncology Through Comprehensive Bibliometric Mapping of Global Trends Impact and Conceptual Structures.Journal of healthcare leadership · 2025Review
- Effectiveness of artificial intelligence for detecting operable lung cancer on chest radiographs.Translational lung cancer research · 2024Article
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7 authors at 1 institution in 1 country.
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Abstract
We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50-0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.
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