Medical image generation based on improved generative adversarial
Aiming at the problems of low and unstable training efficiency of generative adversarial networks and uneven distribution of the original breast cancer dataset in image generation algorithms,this paper proposes an improved SAGAN model,which performs better in the task of generating images,and compared with the traditional SAGAN and GAN,DCGAN models,its key improvement is the use of the ReLU6 activation function and the hinge loss function,instead of the original ReLU activation function and binary balanced cross-entropy loss function,and these improvements improve the quality,diversity and training stability of the generated images.The experimental results show that the D-Loss of the improved SAGAN decreases by 0.114,the mean square error(MSE)decreases by 0.09,and the structural similarity(SSIM)increases by 0.04 compared to the conventional SAGAN.This indicates that the improved SAGAN has an advantage in generating high-quality images and better preserving the image structure.
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