3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification
AbstractLung 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
Share & Cite This Article
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.
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(9):376.Chicago/Turabian Style
Zhang, Guokai; Liu, Xiao; Zhu, Dandan; He, Pengcheng; Liang, Lipeng; Luo, Ye; Lu, Jianwei. 2018. "3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification." Symmetry 10, no. 9: 376.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.