Affiliation:
1. Department of Imaging, The Third Clinical College of Hefei of Anhui Medical University, The Third People’s Hospital of Hefei, Hefei 230022, China
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 (
). The detection rate of CT for stage I of occupational pneumoconiosis was significantly higher than that of DR (
). 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.
Funder
Hefei Independent Innovation Policy “Loan to Supplement” Fund Project
Subject
Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine
Cited by
3 articles.
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