Abstract: Aiming at the difficulty of deep learning model training caused by the lack of labeled lung Computed Tomography (CT) image data and the lung nodule feature model generated by existing generation algorithmsTo solve the problem of blur and detail loss, a data-enhanced RAU-GAN algorithm for pulmonary nodule images is proposed. Firstly, a residual attention module is embedded in the generator network, which can focus on different local regions of interest to achieve the independent generation of lung nodules and background information. Moreover, the residual block structure in the attention module is redesigned to to reduce the depth of the network and training complexity. Second, the discriminator is designed as U-Net architecture, which can feed back more information to the updated generator to improve the discrimination performance. Finally, experiments were conducted on data set LUNA16 and deep lesion. The results show that the visual and different evaluation indexes have improved in comparison with existing methods, which verifies that the generated images can contain richer details. images can contain richer details.