Classification of pulmonary nodules on PET/CT image based on deep belief network / 中国医学影像技术
Chinese Journal of Medical Imaging Technology
;
(12): 77-80, 2020.
Article
in Chinese
| WPRIM
| ID: wpr-861115
ABSTRACT
Objective:
To observe classification effect of pulmonary nodules on PET/CT images with deep belief network (DBN).Methods:
PET/CT images of 216 patients with pulmonary nodules were collected, among them 339 pulmonary nodules were detected, including 190 benign and 149 malignant ones. Totally 2 055 ROI images were captured, incuding 1 069 of benign ones and 986 of malignant ones. Gray scale and size normalization were performed on ROI images, and then the lesions were detected with DBN. The network structure and training parameters were determined by experimentalMethods:
, and theResults:
were evaluated by confusion matrix, overall accuracy, Kappa coefficient and other indicators. A support vector machine model (SVM) was also built with wavelet texture features based on nonsubsampled dual-tree complex contourlet transform (NSDTCT), using the same data as DBN. TheResults:
detected with DBN and SVM were compared.Results:
TheResults:
of DBN and SVMMethods:
were 0.94 and 0.72 for overall accuracy, 0.96 and 0.66 for sensitivity, 0.92 and 0.96 for specificity, and 0.87 and 0.42 for Kappa coefficient, respectively.Conclusion:
The accuracy of DBN in identifying benign and malignant pulmonary nodules is better than that of SVM.
Full text:
Available
Index:
WPRIM (Western Pacific)
Language:
Chinese
Journal:
Chinese Journal of Medical Imaging Technology
Year:
2020
Type:
Article
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