Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy Classification

Lung cancer has been the most prevalent cancer in the world and an effective way to diagnose the cancer at the early stage is to detect the pulmonary nodule by computer-aided system. However, the size of the pulmonary nodules varies and the one with small diameter is generally one of the most difficult cases to diagnose. Under this condition, traditional convolution network based nodule classification methods fail to achieve satisfied result due to the miss of tiny but vital features by the pooling operation. 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 utilize the 3D dilated convolution to capture and preserve more detailed characteristic information of the nodules. Moreover, a multiple receptive field fusion strategy is applied to extract the multi-scale features from the nodule CT images. Extensive experimental results show that our model achieves a better result with an accuracy of 88.6% which outperforms other state-of-the-art methods.