ArticleBMC medical imaging2018
Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method.
Article in BMC medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review.Cureus · 2024Review
- Article
- Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.Journal of cancer research and clinical oncology · 2023Article
- Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection.Computational intelligence and neuroscience · 2023Article
- Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images.Journal of ambient intelligence and humanized computing · 2023Article
- Unsupervised contrastive learning based transformer for lung nodule detection.Physics in medicine and biology · 2022Article
- Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.Neural computing & applications · 2022Review
- Neural architecture search for pneumonia diagnosis from chest X-rays.Scientific reports · 2022Article
- A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection.Sensors (Basel, Switzerland) · 2022Article
- COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN.Diagnostics (Basel, Switzerland) · 2022Article
- Lung nodule malignancy classification with weakly supervised explanation generation.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.Korean journal of radiology · 2021Article
- ALBERT-Based Self-Ensemble Model With Semisupervised Learning and Data Augmentation for Clinical Semantic Textual Similarity Calculation: Algorithm Validation Study.JMIR medical informatics · 2021Article
- 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Review
- Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2020Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately detecting and examining lung nodules early is key in diagnosing lung cancers and thus one of the best ways to prevent lung cancer deaths. Radiologists spend countless hours detecting small spherical-shaped nodules in computed tomography (CT) images. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether they are real nodules. The aim of this paper is to introduce a high performance nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between non-nodules.
methodsIn this paper, we use a three dimensional deep convolutional neural network (3D DCNN) with shortcut connections and a 3D DCNN with dense connections for lung nodule classification. The shortcut connections and dense connections successfully alleviate the gradient vanishing problem by allowing the gradient to pass quickly and directly. Connections help deep structured networks to obtain general as well as distinctive features of lung nodules. Moreover, we increased the dimension of DCNNs from two to three to capture 3D features. Compared with shallow 3D CNNs used in previous studies, deep 3D CNNs more effectively capture the features of spherical-shaped nodules. In addition, we use an alternative ensemble method called the checkpoint ensemble method to boost performance.
resultsThe performance of our nodule classification method is compared with that of the state-of-the-art methods which were used in the LUng Nodule Analysis 2016 Challenge. Our method achieves higher competition performance metric (CPM) scores than the state-of-the-art methods using deep learning. In the experimental setup ESB-ALL, the 3D DCNN with shortcut connections and the 3D DCNN with dense connections using the checkpoint ensemble method achieved the highest CPM score of 0.910.
conclusionThe result demonstrates that our method of using a 3D DCNN with shortcut connections, a 3D DCNN with dense connections, and the checkpoint ensemble method is effective for capturing 3D features of nodules and distinguishing nodules between non-nodules.
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