Affiliations: [a] North China University of Technology, School of Electrical Information, Beijing, China
| [b] School of Software Engineering, Beihang University, Beijing, China
| [c] Department of Engineering Science and Physics, City University of New York at CSI, Staten Island, NY, USA
| [d] Department of Biomedical, Northeast University, Shenyan, China
| [e] Department of Radiology, State University of New York at Stony Brook, NY, USA
Correspondence:
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Corresponding author: Huafeng Wang, North China University of Technology, School of Electrical Information, Beijing, 100000, China. E-mail: wanghuafengbuaa@gmail.com.
Abstract: The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.
