Early detection of lung cancer is a crucial step to improve the chances of survival. To detect the pulmonary nodules, various methods are proposed including one-stage object detection methods (e.g., YOLO, SSD) and two-stage detection methods(e.g., Faster RCNN). Two-stage methods are more accurate than one-stage, thus more likely used in the detection of a small object. Faster RCNN as a two-stage method, ensuring more efficient and accurate region proposal generation, is consistent with our task's objective, that is, detecting small 3-D nodules from large CT image volume. Therefore, in our work, we used 3-D region proposal network (RPN) proposed in Faster RCNN to detect nodules. However, different from natural images with clear boundaries and textures, pulmonary nodules have different types and locations, which are hard to recognize. Thus with the thought that if the network can learn more features of the nodules, the performance would be better, we also applied the "Squeeze-and-Excitation"blocks to the 3-D RPN, which we term it as SE-Res RPN. The experimental results show that the sensitivity of SE-Res RPN in 10-fold cross-validation of LUNA 16 is 93.7, which achieves great performance without a false positive reduction stage.