3D SAACNet with GBM for the classification of benign and malignant lung nodules. (February 2023)

• Record Type: Journal Article
• Authors: Guo, Zhitao; Yang, Jikai; Zhao, Linlin; Yuan, Jinli; Yu, Hengyong
• Is Part Of: Computers in biology and medicine. Volume 153(2023)
• Publication Date: 2023-02
• Subjects: Image processing -- Lung nodule classification -- Asymmetric convolution -- Multiscale -- Attention mechanism; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106532
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification. Highlights: Design a pulmonary nodule detection and classification network based on the 3D model and GBM. Use asymmetric convolution to extract fine-grained multiscale spatial information. Use the dual-path connection mode for feature reuse. Pay more attention to the difficult samples by the adjustment factor in loss function.