In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.

中文翻译：

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基于改进卷积神经网络的新冠肺炎CT图像分析及临床诊断

本文基于改进的卷积神经网络，对新冠肺炎CT图像进行深度分析，利用U-Net系列深度神经网络对新冠肺炎CT图像进行语义分割，得到新冠肺炎区域作为前景，其余区域作为二值图像的背景，为后续的图像诊断提供依据。其次，目标检测框架Faster RCNN从新冠肺炎肿瘤的CT图像中提取特征，获得数据更高层次的抽象表示，确定新冠肺炎肿瘤的病灶位置，并给出其边界框在图片。通过生成对抗网络来诊断新冠肺炎肿瘤CT图像的病灶区域，