Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction
Computational and Mathematical Methods in Medicine
and Objective. Effective segmentation of pulmonary nodules can effectively assist in the diagnosis of benign and malignant pulmonary nodules. We aim to explore the effectiveness of classification and segmentation algorithms in diagnosing benign and malignant pulmonary nodules under different CT reconstructions. Methods. The imaging data of 55 patients with chest CT plain scan in the Xuancheng People's Hospital were collected retrospectively. The data of each patient included lung window
... uction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance. Results. Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were 80.32 % ± 5.91 % , 79.83 % ± 6.12 % , and 80.17 % ± 5.89 % , respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all P > 0.05 ). Conclusion. The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.