ArticleJournal of clinical medicine2022
Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 43 citations in OpenAlex.
- Brain Tumor Classification in MRI Images Using Combined Transfer Learning and Convolutional Neural Networks.Journal of imaging · 2026Article
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- PredictMed-CDSS: Artificial Intelligence-Based Decision Support System Predicting the Probability to Develop Neuromuscular Hip Dysplasia.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data.Journal of imaging · 2025Article
- Stacking model framework reveals clinical biochemical data and dietary behavior features associated with type 2 diabetes: A retrospective cohort study.APL bioengineering · 2024Article
- A Novel Model Based on CNN-ViT Fusion and Ensemble Learning for the Automatic Detection of Pes Planus.Journal of clinical medicine · 2024Article
- 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
- Deep convolutional network-based chest radiographs screening model for pneumoconiosis.Frontiers in medicine · 2024Article
- MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor.Journal of imaging · 2023Article
- Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 5 institutions in 4 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.
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