Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs.
This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung...
| Published in: | Journal of Digital Imaging Vol. 27; no. 1; pp. 90 - 98 |
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| Main Authors: | , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2014
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104013603&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104013603 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2014 vid: 27 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104013603 94061944 10.1007/s10278-013-9620-9 NLM23836078 PMC3903963 104013603 ppf: 90 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Support Vector Machine Model for Diagnosing Pneumoconiosis Based on Wavelet Texture Features of Digital Chest Radiographs. aug: au: Zhu, Biyun Chen, Hui Chen, Budong Xu, Yan Zhang, Kuan affil: School of Biomedical Engineering, Capital Medical University, No. 10 Xitoutiao, YouAnMen Beijing 100069 China sug: subj: Pneumoconiosis Diagnosis Radiography, Thoracic Radiography, Computed Radiographic Image Interpretation, Computer-Assisted Methods Pneumoconiosis Radiography Decision Trees Diagnosis, Computer Assisted Validation Studies ROC Curve P-Value Sensitivity and Specificity Algorithms Pearson's Correlation Coefficient Human Funding Source ab: This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 ( P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 ( P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 ( P = 0.37), 0.95 ± 0.02 ( P = 0.24), and 0.90 ± 0.03 ( P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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