3D multi-view convolutional neural networks for lung nodule classification

PLoS One. 2017 Nov 16;12(11):e0188290. doi: 10.1371/journal.pone.0188290. eCollection 2017.

Abstract

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.

MeSH terms

  • Algorithms
  • Humans
  • Lung Neoplasms / diagnostic imaging*
  • Neural Networks, Computer*
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Tomography, X-Ray Computed

Grants and funding

This work was supported by National Natural Science Foundation of China (61471064) to Guixia Kang and National High Technology Research and Development Program of China (“863” Program, No. 2015AA01A709) to Guixia Kang. Beijing Ciji Network Technology Co., Ltd provided support in the form of a salary for author Guixia Kang, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section. The funder provided support in the form of salaries for authors [Guixia Kang], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.