Abstract: For solving the problem of limited model learning ability caused by too little labeled medical image data and easy loss of tiny nodule features caused by sub-sampling in the process of deep detection, a lung CAD system based on CT-GAN data augmentation and improved YOLO-V4 detection framework was designed. In the first part, the regularization method DropBlock was introduced into the nodule generation algorithm CT-GAN to augment the data of annotated medical images, so as to improve the generation quality of pulmonary nodules.In the second part, attention model was introduced in YOLO-V4, and CA-YOLO-V4 framework was constructed to capture the position perception, direction perception and cross-channel information of pulmonary nodules, helped the model to detect the region of interest of pulmonary nodules more accurately. The experimental results show that the performance indexes of data augmentation and nodule detection in LUNA16 data set of the proposed lung CAD system are superior to the comparison algorithm, which can effectively expand the data set and improve the performance of nodule detection.

Key words: Lung Computed Aided Detection, Data Augmentation, Pulmonary nodule detection, CA-YOLO-V4, The improved CT-GAN