ArticleBiomedical engineering online2018
CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Automated Collateral Classification on CT Angiography in Acute Ischemic Stroke: Performance Trends Across Hyperparameter Combinations.Bioengineering (Basel, Switzerland) · 2026Article
- A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTsCurrent medical imaging · 2025Article
- Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules.Quantitative imaging in medicine and surgery · 2023Article
- LncRNA FAM138B inhibits the progression of non-small cell lung cancer through miR-105-5p.Cell cycle (Georgetown, Tex.) · 2023Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification.Sensors (Basel, Switzerland) · 2022Article
- An Effective Approach for Automated Lung Node Detection using CT Scans.Journal of biomedical physics & engineering · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Seven tumor-associated autoantibodies as a serum biomarker for primary screening of early-stage non-small cell lung cancer.Journal of clinical laboratory analysis · 2021Article
- Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.The international journal of cardiovascular imaging · 2021Article
- Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence.Frontiers in oncology · 2021Article
- A bilinear convolutional neural network for lung nodules classification on CT images.International journal of computer assisted radiology and surgery · 2021Article
- Texture recognition of pulmonary nodules based on volume local direction ternary pattern.Bioengineered · 2020Article
- Scoring pleurisy in slaughtered pigs using convolutional neural networks.Veterinary research · 2020Article
- Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning.Computational intelligence and neuroscience · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundEarly and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for detecting micro-nodules (diameter < 3 mm), which increases the radiologists' workload and causes unnecessary anxiety for the patients. To decrease the false positive rate, we propose to use CNN models to discriminate between pulmonary micro-nodules and non-nodules from CT image patches.
methodsA total of 13,179 micro-nodules and 21,315 non-nodules marked by radiologists are extracted with three different patch sizes (16 × 16, 32 × 32 and 64 × 64) from LIDC/IDRI database and used in the experiments. Three CNN models with different depths (1, 2 or 4 convolutional layers) are designed; their performances are evaluated by the fivefold cross-validation in term of the accuracy, area under the curve (AUC), F-score and sensitivity. The network parameters are also optimized.
resultsIt is found that the performance of the CNN models is greatly dependent on the patches size and the number of convolutional layers. The CNN model with two convolutional layers presented the best performance in case of 32 × 32 patches size, achieving an accuracy of 88.28%, an AUC of 0.87, a F-score of 83.45% and a sensitivity of 83.82%.
conclusionsThe CNN models with appropriate depth and size of image patches can effectively discriminate between pulmonary micro-nodules and non-nodules, and reduce the false positives and help manage lung cancer precisely.
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