ACRnet: Adaptive Cross-transfer Residual neural network for chest X-ray images discrimination of the cardiothoracic diseases

Abstract

Cardiothoracic diseases are a serious threat to human health and chest X-ray image is a great reference in diagnosis and treatment. At present, it has been a research hot-spot how to recognize chest X-ray image automatically and exactly by the computer vision technology, and many scholars have gotten the excited research achievements. While both emphysema and cardiomegaly often are associated, and the symptom of them are very similar, so the X-ray images discrimination for them led easily to misdiagnosis too. Therefore, some efforts are still expected to develop a higher precision and better performance deep learning model to recognize efficiently the two diseases. In this work, we construct an adaptive cross-transfer residual neural network (ACRnet) to identify emphysema, cardiomegaly and normal. We cross-transfer the information extracted by the residual block and adaptive structure to different levels in ACRnet, and the method avoids the reduction of the adaptive function by residual structure and improves the recognition performance of the model. To evaluate the recognition ability of ACRnet, four neural networks VGG16, InceptionV2, ResNet101 and CliqueNet are used for comparison. The results show that ACRnet has better recognition ability than other networks. In addition, we use the deep convolution generative adversarial network (DCGAN) to expand the original dataset and ACRnet's recognition ability is greatly improved.

Keywords

• convolutional neural network
• chest x-ray image
• deep convolution generative adversarial networks
• cardiothoracic diseases