The morbidity and mortality of lung cancer are increasing rapidly in every country in the world, and pulmonary nodules are the main symptoms of lung cancer in the early stage. If we can diagnose pulmonary nodules in time at the early stage and follow up and treat suspicious patients, we can effectively reduce the incidence of lung cancer. CT (Computed Tomography) has been applied to the screening of many diseases because of its high resolution. Pulmonary nodules show white round shadows in CT images. With the popularity of CT equipment, doctors need to review a large number of imaging results every day. Doctors will misjudge and miss the lesions because of reviewing CT scanning results for a long time. At this time, the method of automatic detection of pulmonary nodules by computer can relieve the pressure of doctors in reviewing CT scan results. Traditional lung nodule detection methods, such as gray threshold method and region growing method, divide the detection process into two steps: extracting candidate regions and eliminating false regions. In addition, the traditional detection method can only operate on a single image, which leads to the inability of this method to detect the batch scanning results in real time. With the continuous development of computer equipment performance and artificial intelligence, the relationship between medical image processing and deep learning is getting closer and closer. In deep learning, object detection methods such as Faster R-CNN、YOLO can complete parallel detection of batch images, and deep structure can fully extract the features of input images. Compared with traditional lung nodule detection methods, it has the characteristics of high efficiency and high precision. Faster R-CNN is a classical and high-precision two-stage object detection method. In this paper, an improved Faster R-CNN model is proposed. On the basis of Faster R-CNN, multi-scale training strategy is used to fully mine the features of different scale spaces and perform path augmentation on lower-dimensional features, which improves the small object detection ability of the model. Through Online Hard Example Mining (OHEM), the loss value is used to quantify the difficulty of candidate region detection, and the training times of the region to be detected are adaptively adjusted. Make full use of prior information to customize the size and proportion of preset boundary anchor boxes. Using deformable convolution to improve the visual field to enhance the global features and enhance the ability to extract the feature information of pulmonary nodules in the same scale space. The new model was tested on LUNA16 (Lung Nodule Analysis 2016) dataset. The detection precision of the improved Faster R-CNN model for pulmonary nodules increased from 76.4% to 90.7%, and the recall rate increased from 40.1% to 56.8% Compared with the mainstream object detection algorithms YOLOv3 and Cascade R-CNN, the improved model is superior to the above models in every index.