ArticleJournal of digital imaging2017
Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.
Article in Journal of digital imaging, 2017. 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.
- Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- A population-based retrospective cohort study of 4,103 pneumoconiosis cases in Zhenjiang, China, 1965-2025: a survival analysis.Frontiers in public health · 2026Article
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.BMC medical imaging · 2024Article
- Survival and disease burden analyses of occupational pneumoconiosis during 1958-2021 in Huangshi city, China: a retrospective cohort study.BMC public health · 2024Article
- Artificial intelligence in advancing occupational health and safety: an encapsulation of developments.Journal of occupational health · 2024Review
- Deep convolutional network-based chest radiographs screening model for pneumoconiosis.Frontiers in medicine · 2024Article
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
- Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning.Frontiers in medicine · 2024Article
- LDADN: a local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection.Biomedical optics express · 2022Article
- Intelligent Image Diagnosis of Pneumoconiosis Based on Wavelet Transform-Derived Texture Features.Computational and mathematical methods in medicine · 2022Article
- Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.BMC medical imaging · 2021Article
- Balanced Convolutional Neural Networks for Pneumoconiosis Detection.International journal of environmental research and public health · 2021Article
- Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2020Article
- Detection of Lung Contour with Closed Principal Curve and Machine Learning.Journal of digital imaging · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.
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