Automatic detection of pulmonary nodules in CT images based on 3D Res-I network

Abstract

It is difficult for the existing detection methods of the pulmonary nodules to take into account the global and local features simultaneously. It will lead to over-fitting and lower sensitivity since the extracted features of 3D pulmonary nodules is too complex. To solve these problems, a model based an improved 3D residual structure (3D Res-I) was proposed to detect pulmonary nodules. In the model, the basic residual structure is improved by using rectangular convolution kernel, grouping convolution and pre-activation. Rectangular convolution kernel expands the receptive filed of the convolution, which effectively takes into account the global and local features of the pulmonary nodules. Grouping convolution reduces the computational cost of the model. Pre-activation operation alleviates over-fitting phenomenon. 3D Res-I structure is combined with the improved U-Net network as the feature extraction network of Faster R-CNN. The experimental results on LUNA16 dataset show that the proposed model improves the detection accuracy of pulmonary nodules and reduces the average number of false positives and the size of the generated model.