ArticleBMC medical imaging2024
Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Development of a machine learning-based risk prediction model for early-stage pneumoconiosis: a retrospective study.Frontiers in medicine · 2025Article
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7 authors.
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Abstract
backgroundPneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoconiosis based on a multi-stage joint deep learning approach using X-ray chest radiographs of pneumoconiosis patients.
methodsIn this study, a total of 498 medical chest radiographs were obtained from the Department of Radiology of West China Fourth Hospital. The dataset was randomly divided into a training set and a test set at a ratio of 4:1. Following histogram equalization for image enhancement, the images were segmented using the U-Net model, and staging was predicted using a convolutional neural network classification model. We first used Efficient-Net for multi-classification staging diagnosis, but the results showed that stage I/II of pneumoconiosis was difficult to diagnose. Therefore, based on clinical practice we continued to improve the model by using the Res-Net 34 Multi-stage joint method.
resultsOf the 498 cases collected, the classification model using the Efficient-Net achieved an accuracy of 83% with a Quadratic Weighted Kappa (QWK) score of 0.889. The classification model using the multi-stage joint approach of Res-Net 34 achieved an accuracy of 89% with an area under the curve (AUC) of 0.98 and a high QWK score of 0.94.
conclusionsIn this study, the diagnostic accuracy of pneumoconiosis staging was significantly improved by an innovative combined multi-stage approach, which provided a reference for clinical application and pneumoconiosis screening.
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