ReviewSensors (Basel, Switzerland)2019
An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images.
Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Efficacy of pulmonary rehabilitation combined with minimal-dose adjunctive TCM (Shen Yi Capsules and Hua Chan Su) in improving postoperative outcomes for non-small cell lung cancer: a randomized controlled trial.Frontiers in medicine · 2026Article
- 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
- A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Article
- Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model.Multimedia tools and applications · 2022Article
- VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.Diagnostics (Basel, Switzerland) · 2021Article
- Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs.Journal of digital imaging · 2021Article
- Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.Quantitative imaging in medicine and surgery · 2021Article
- Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules.Journal of thoracic disease · 2020Review
- 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Overview of radiomics in breast cancer diagnosis and prognostication.Breast (Edinburgh, Scotland) · 2020Review
- Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.Journal of Cancer · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.