SynthesisJournal of digital imaging2020
Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.
Synthesis in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 109 citations in OpenAlex.
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.PloS one · 2026Article
- Application value of high-resolution CT imaging features combined with texture analysis in patients with solitary pulmonary nodules.Frontiers in oncology · 2026Article
- Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.Nature biomedical engineering · 2025Article
- CSEA-Net: A channel-spatial enhanced attention network for lung tumor segmentation on CT images.iScience · 2025Article
- The feasibility and cost-effectiveness of implementing mobile low-dose computed tomography with an AI-based diagnostic system in underserved populations.BMC cancer · 2025Article
- Advantages and rational application of indocyanine green fluorescence in pulmonary nodule surgery: a narrative review.Journal of thoracic disease · 2024Review
- Differential diagnosis of congenital ventricular septal defect and atrial septal defect in children using deep learning-based analysis of chest radiographs.BMC pediatrics · 2024Article
- Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation.European radiology · 2024Article
- Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.International journal of computer assisted radiology and surgery · 2024Article
- Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.Thoracic cancer · 2024Article
- Advances in early detection of non-small cell lung cancer: A comprehensive review.Cancer medicine · 2024Review
- Application of convolutional neural networks in medical images: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.International journal of computer assisted radiology and surgery · 2024Article
- Establishing and validation of the VBV score for assessing Lung ground-glass nodules based on high-resolution computed tomography.Journal of cardiothoracic surgery · 2024Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- Article
- An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier.Journal of cancer research and clinical oncology · 2023Article
- Multi-scale detection of pulmonary nodules by integrating attention mechanism.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
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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.