基于深度信念网络检测PET/CT图像肺结节良恶性

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 experimental Methods:, and the Results: 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. The Results: detected with DBN and SVM were compared. Results: The Results: of DBN and SVM Methods: 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.