• 图形图像 •

### 基于CNN多层面二阶特征融合的肺结节分类

1. 1. 昆明理工大学 信息工程与自动化学院，昆明 650500
2. 云南省计算机技术应用重点实验室，昆明 650500
• 出版日期:2020-09-01 发布日期:2020-09-07

### Classification of Pulmonary Nodules Based on CNN Multi-level Second-order Feature Fusion

LI Wei, ZHAO Xiaole, DUAN Yanlong, LIU Lijun, HUANG Qingsong

1. 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
2. Yunnan Key Laboratory of Computer Technology Applications, Kunming 650500, China
• Online:2020-09-01 Published:2020-09-07

Abstract:

The CT image of the lung has a large number of slices. The positions and shapes of the lung nodules in the CT image are different, and the environment around the lung nodules is complex. Traditional methods usually detect pulmonary nodules only by the shape, gray scale and other characteristics, with low utilization rate of pul-monary nodules characteristic information, and do not fully consider the fine-grained characteristics of lung nodules. This paper designs a multi-level second-order feature fusion model based on convolutional neural network (CMSFF). The convolution neural network is used to extract the features of the multi-level sections of the same nodule. Through the two-stage feature fusion, the fine-grained features of the pulmonary nodules are fully considered to achieve accurate extraction of the lung nodule feature information. Experimental results show that this paper reaches an AUC value of 0.924 in the classification of malignant degree of pulmonary nodules through the characteristic information extracted by this method which can effectively improve the classification accuracy of malignant degree of pulmonary nodules.