Lung Nodule Data Enhancement Algorithm Based on Generative Adversarial Network

Abstract

Due to the strong privacy of data and the difficulty of annotation, the amount of medical image data is relatively small, which further affects the effect of deep learning. Using data enhancement technology to expand existing data sets can significantly alleviate the problem of insufficient data. Deep Convolutional generative adversarial Network (DCGAN) technology has been widely used in the field of medical image data enhancement, but there are still some problems such as unstable training, difficulty in convergence, easy to produce mode collapse and insufficient quality of the generated images. In this paper, the mode collapse problem of deep convolutional generative adversarial network (DCGAN) is improved by using the fusion attention mechanism and modifying the loss function. Experiments show that the method proposed in this paper can effectively improve the enhancement effect of lung nodule image data.