Enhancement of Chest X-ray Image Data by Using Fast convergence GAN Based on Multi-Dimensional Convolution and Residual Unit

Aiming at the data enhancement method in deep learning, the Generative adversarial networks (GAN) model is improved to form a fast convergence generation-type confrontation network, which can overcome the problems of unstable GAN training process, slow convergence speed and easy pattern collapse. . The multi-size convolution is used in the discriminator to enhance the feature extraction ability of the discriminator; the method of adding the residual unit in the generator enables the generator to quickly fit the distribution of the real data; and pre-training the discriminator The strategy is beneficial to improve the stability of the pre-build training and speed up the training process. Experiments with CIFAR- 10 standard datasets, compared with several GAN-based models, confirm that the improved algorithm is better and the image quality and diversity are better. The improved algorithm proposed in this paper was used to generate the extended samples from the chest X-ray dataset of the NIH clinical database in the United States. The validity of the algorithm was confirmed by Turing test.