ArticlePhysics in medicine and biology2011
Computer-aided detection of early interstitial lung diseases using low-dose CT images.
Article in Physics in medicine and biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of rheumatoid arthritis-associated interstitial lung disease: a systematic review and meta-analysis.BMC pulmonary medicine · 2026Pooled it
- Drug-induced interstitial lung disease caused by gefitinib in the treatment of non-small cell lung cancer: A case report.Molecular and clinical oncology · 2025Article
- ILDIM-MFAM: interstitial lung disease identification model with multi-modal fusion attention mechanism.Frontiers in medicine · 2024Article
- Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.Bioengineering (Basel, Switzerland) · 2023Article
- An Effective Approach for Automated Lung Node Detection using CT Scans.Journal of biomedical physics & engineering · 2022Article
- Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks.Frontiers in oncology · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients.Life (Basel, Switzerland) · 2021Article
- Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.Scientific reports · 2020Article
- A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans.Journal of healthcare engineering · 2018Article
- Improving Care for Patients with Interstitial Lung Disease Using Machine Learning Requires Transparency and Reproducibility.Annals of the American Thoracic Society · 2017Article
- A sparse representation based method to classify pulmonary patterns of diffuse lung diseases.Computational and mathematical methods in medicine · 2015Article
- Application of clinico-radiologic-pathologic diagnosis of diffuse parenchymal lung diseases in children in China.PloS one · 2015Article
- Automated 3D ιnterstitial lung disease εxtent quantification: performance evaluation and correlation to PFTs.Journal of digital imaging · 2014Article
- [Interstitial lung disease associated with lung cancer treatment].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2013Review
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Authors and funding
7 authors.
Funding
Abstract
This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.
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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.