Abstract: To solve the problem of limited model learning ability caused by insufficient labeled medical image data and easy loss of tiny nodule features caused by sub-sampling in the process of deep detection, a lung computer aided-detection system based on a generative adversarial network based on computed tomography data augmentation and improved you only look once-V4 (YOLO-V4) detection framework is designed. First, the regularization method DropBlock is introduced into the nodule generation framework computed tomography-generative adversarial networks to augment the data of annotated medical images, which can improve the generation quality of pulmonary nodules. Second, the coordinate attention model is introduced in YOLO-V4, which was constructed to capture the position perception, direction perception and cross-channel information of pulmonary nodules, which can further help 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 the lung nodule analysis 16 data set of the proposed lung computer aided detection system are superior to the comparison algorithm, which can effectively expand the data set and improve the performance of nodule detection.

Key words: lung computer aided detection system, data augmentation, lung nodule detection