ArticleComputational and mathematical methods in medicine2022
Intelligent Image Diagnosis of Pneumoconiosis Based on Wavelet Transform-Derived Texture Features.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- A multimodal deep learning framework for precise silicosis detection on radiographic images.Scientific reports · 2026Article
- Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.Insights into imaging · 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
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Objective: Early diagnosis and treatment of occupational pneumoconiosis can delay the development of the disease. This study is aimed at investigating the intelligent diagnosis of occupational pneumoconiosis by wavelet transform-derived entropy. Method: From June 2013 to June 2020, the high KV digital radiographs (DR) and computed tomography (CT) images from a total of 60 patients with occupational pneumoconiosis in our department were selected. The wavelet transform-derived texture features were extracted from all images, and the decision tree was used for feature selection. The support vector machines (SVM) with three kernel functions were selected to classify the two kinds of images, and their diagnostic efficiency was compared. Result: After eight times of wavelet decomposition, eight wavelet entropy texture features (feature set) were extracted, and six were selected to form the feature subset. The classification effect of linear kernel function SVM is better than those of other functions, with an accuracy of 84.2%. The diagnostic values of DR and CT for occupational pneumoconiosis were the same (kappa = 0.737, Conclusion: It is helpful to improve the early diagnosis level of pneumoconiosis by using SVM to make an intelligent diagnosis based on the wavelet entropy.
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