ArticleMedical physics2021
Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 48 citations in OpenAlex.
- A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.Scientific reports · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.Cancer medicine · 2024Review
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology.Physical and engineering sciences in medicine · 2023Article
- Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets.Heliyon · 2023Article
- Multi-scale detection of pulmonary nodules by integrating attention mechanism.Scientific reports · 2023Article
- A Novel Deep Learning Model Based on Multi-Scale and Multi-View for Detection of Pulmonary Nodules.Journal of digital imaging · 2023Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- Artificial intelligence in lung cancer: Application and future thinking.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- DNL-Net: deformed non-local neural network for blood vessel segmentation.BMC medical imaging · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- Automatic estimation of knee effusion from limited MRI data.Scientific reports · 2022Article
- Practice toward standardized performance testing of computer-aided detection algorithms for pulmonary nodule.Frontiers in public health · 2022Article
- Computational approaches to detect small lesions inJournal of applied clinical medical physics · 2021Article
- Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
purposeEarly detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes.
methodsThe nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment.
resultsThe proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane.
conclusionOur approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
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