ArticleJournal of medical systems2017
An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images.
Article in Journal of medical systems, 2017. 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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Who cites it
16 citing papers in PubMed.
- Has computer-aided diagnosis now outperformed radiologists in the accurate localization of lung nodules?-a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification.Journal of digital imaging · 2023Article
- The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review.Journal of clinical medicine · 2023Review
- Diagnostic efficiency of artificial intelligence for pulmonary nodules based on CT scans.American journal of translational research · 2023Article
- A convolutional neural network-based COVID-19 detection method using chest CT images.Annals of translational medicine · 2022Article
- CAD system for lung nodule detection using deep learning with CNN.Medical & biological engineering & computing · 2022Article
- Accurate segmentation for different types of lung nodules on CT images using improved U-Net convolutional network.Medicine · 2021Article
- Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM.Computational intelligence and neuroscience · 2021Article
- Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images.Journal of digital imaging · 2020Article
- Survey on deep learning for pulmonary medical imaging.Frontiers of medicine · 2020Review
- [Pulmonary nodule detection method based on convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
- CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.Biomedical engineering online · 2018Article
- Performance analysis of a computer-aided detection system for lung nodules in CT at different slice thicknesses.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning.IEEE journal of translational engineering in health and medicine · 2018Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.
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Registered trials
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