改进卷积神经网络的COVID-19CT影像分类方法研究

针对2019年12月在中国武汉发现的新型冠状病毒,由于RT-PCR检测具有假阴性率过高且得出结果会花费大量时间等问题,研究证明计算机断层扫描(CT)已经成为了辅助诊断和治疗新型冠状病毒肺炎的重要手段之一.由于目前公开的COVID-19 CT数据集较少,提出利用条件生成对抗网络进行数据增强以获得更多样本的CT数据集,以此...

Research on COVID-19 CT Image Classification Method Based on Improved Convolutional Neural Network

In response to the COVID-19 discovered in Wuhan, China in December 2019, due to the high false-negative rate of RT-PCR testing and the fact that it takes a lot of time to get the results, research has proved that computer tomography（CT） has become one of the important methods to assist in the diagnosis and treatment of COVID-19. Since there are currently fewer COVID-19 CT data sets publicly available, this paper proposes to use conditional generative adversarial networks for data enhancement to obtain more sample CT data sets, so as to reduce the risk of overfitting. In addition, an improved U-Net network based on BIN residual blocks is proposed to perform image segmentation, and then combined with multi-layer perceptrons for classification prediction. By comparing with network models such as AlexNet and GoogleNet, it is concluded that the BUF-Net network model proposed in this paper has the best performance, reaching an accuracy of 93%. Finally, the Grad-CAM technology is used to visualize the output of the system, which can more intuitively explain the important role of CT images in the diagnosis of COVID-19. The application of deep learning technology to medical images helps radiologists to obtain more effective diagnosis.