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Symmetry 2018, 10(9), 376; https://doi.org/10.3390/sym10090376

3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification

1
School of Software Engineering, Tongji University, Shanghai 201804, China
2
Institute of Translational Medicine, Tongji University, Shanghai 201804, China
*
Authors to whom correspondence should be addressed.
Received: 6 August 2018 / Revised: 18 August 2018 / Accepted: 22 August 2018 / Published: 1 September 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
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Abstract

Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem, we propose a novel 3D spatial pyramid dilated convolution network to classify the malignancy of the pulmonary nodules. Instead of using the pooling layers, we use 3D dilated convolution to learn the detailed characteristic information of the pulmonary nodules. Furthermore, we show that the fusion of multiple receptive fields from different dilated convolutions could further improve the classification performance of the model. Extensive experimental results demonstrate that our model achieves a better result with an accuracy of 88.6 % , which outperforms other state-of-the-art methods. View Full-Text
Keywords: computer aided system; pulmonary nodule; dilated convolution; malignancy classification computer aided system; pulmonary nodule; dilated convolution; malignancy classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhang, G.; Liu, X.; Zhu, D.; He, P.; Liang, L.; Luo, Y.; Lu, J. 3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification. Symmetry 2018, 10, 376.

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