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

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  • Guixia Kang
  • Kui Liu
  • Beibei Hou
  • Ningbo Zhang

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.

Suggested Citation

  • Guixia Kang & Kui Liu & Beibei Hou & Ningbo Zhang, 2017. "3D multi-view convolutional neural networks for lung nodule classification," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
  • Handle: RePEc:plo:pone00:0188290
    DOI: 10.1371/journal.pone.0188290
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    Cited by:

    1. Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Siyuan Tang & Min Yang & Jinniu Bai, 2020. "Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-27, August.

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