ArticleInternational journal of environmental research and public health2021
Balanced Convolutional Neural Networks for Pneumoconiosis Detection.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A multimodal deep learning framework for precise silicosis detection on radiographic images.Scientific reports · 2026Article
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- PGC-1 alpha regulates mitochondrial biogenesis to promote silica-induced pulmonary fibrosis.Toxicology research · 2025Article
- Determining Thresholds for Computer-Aided Detection for Silicosis-An Analytic Approach.American journal of industrial medicine · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.Frontiers in public health · 2024Review
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
- Deep convolutional network-based chest radiographs screening model for pneumoconiosis.Frontiers in medicine · 2024Article
- 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
- Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis.BMC pulmonary medicine · 2022Observational
- Early Identification, Accurate Diagnosis, and Treatment of Silicosis.Canadian respiratory journal · 2022Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.
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