In order to solve the problem that low-dose computerized tomography (CT) introduces a lot of noise in the proqcess of image acquisition,which leads to the serious degradation of image quality,a low-dose CT denoising algorithm based on residual attention mechanism and composite perceptual loss is proposed in this paper.In this algorithm,the Generative Adversarial Networks is used to complete the denoising of low-dose CT images.The multi-scale feature extraction and residual attention module are introduced into the network framework to fuse the information of different scales in the image,improve the ability of the network to distinguish noise features,and avoid the loss of image details in the process of denoising.At the same time,the composite perceptual loss function is used to accelerate the convergence speed of the network and promote the denoising image to approach the original image perceptually.Experimental results show that the proposed algorithm can effectively suppress noise and recover more texture details in low-dose CT images compared with existing algorithms.Compared with the low-dose CT images,the peak signal-to-noise ratio (PSNR) value and structural similarity (SSIM)value of the CT images processed by the proposed algorithm are increased by 31.72% and 13.15%,which can meet the higher requirements of medical imaging diagnosis.