ReviewDiagnostics (Basel, Switzerland)2022
Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- An Expert Committee-Driven Adaptive Framework for Multicenter Pulmonary Nodule Classification Using AI-Derived Quantitative CT Features.Journal of imaging informatics in medicine · 2026Article
- Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.Journal of Korean medical science · 2026Article
- MTRFU-Net: a lung nodule segmentation model based on improved U-Net architecture with spatial-frequency fusion.BMC medical imaging · 2026Article
- CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.Annals of surgical oncology · 2026Article
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Intelligent Attention-Driven Deep Learning for Hip Disease Diagnosis: Fusing Multimodal Imaging and Clinical Text for Enhanced Precision and Early Detection.Medicina (Kaunas, Lithuania) · 2026Article
- Application of an improved YOLOv5s-based deep learning model for automated detection of pulmonary adenocarcinomaFrontiers in medicine · 2026Article
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.Japanese journal of radiology · 2025Article
- A deep learning-based computed tomography reading system for the diagnosis of lung cancer associated with cystic airspaces.Scientific reports · 2025Article
- Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.BMC medical imaging · 2025Article
- MSA-Net: multiple self-attention mechanism for 3D lung nodule classification in CT images.BMC medical imaging · 2025Article
- External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.Japanese journal of radiology · 2025Article
- Evaluating Super-Resolution Models in Biomedical Imaging: Applications and Performance in Segmentation and Classification.Journal of imaging · 2025Article
- Artificial intelligence in automated detection of lung nodules: a narrative review.International journal of physiology, pathophysiology and pharmacology · 2025Review
- EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy.Cancers · 2024Article
- Advantages and rational application of indocyanine green fluorescence in pulmonary nodule surgery: a narrative review.Journal of thoracic disease · 2024Review
- 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
- Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.Journal of imaging informatics in medicine · 2024Article
- A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation.Scientific data · 2024Article
Corrections and comments
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5 authors.
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Abstract
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
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