ArticleBMC medical imaging2021
Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.Scientific data · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.Frontiers in medicine · 2025Article
- Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.Journal of imaging informatics in medicine · 2024Article
- The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmonary Pathology Working Group.Virchows Archiv : an international journal of pathology · 2024Review
- Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.BMC medical imaging · 2024Article
- DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.Scientific reports · 2024Article
- [A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Pneumonia detection based on RSNA dataset and anchor-free deep learning detector.Scientific reports · 2024Article
- Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning.Frontiers in medicine · 2024Article
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
- Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms.The clinical respiratory journal · 2023Article
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
- Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.Journal of clinical medicine · 2022Article
- Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis.BMC pulmonary medicine · 2022Observational
- Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma.Computational intelligence and neuroscience · 2022Article
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10 authors.
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Abstract
purposeThe objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. MATERIALS AND
methods1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people.
resultsAmong the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively.
conclusionThe successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.
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