Abstract:

In the detection of COVID-19, chest X-ray (CXR) images and CT scan images are two main technical methods, which provide an important basis for doctors' diagnosis. Currently, convolutional neural network (CNN) in detecting the COVID-19 medical radioactive images has problems of low accuracy, complex algorithms, and inability to mark feature regions. In order to solve these problems, this paper proposes an algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN). The algorithm can quickly classify lung X-ray images and CT scan images of COVID-19-positive patients, COVID-19-negative patients, general pneumonia patients and healthy people. At the same time, it can quickly locate the critical area in X-ray images and CT images. Finally, the algorithm can get more accurate detection results through the synthesis of deep learning algorithms. In order to verify the effectiveness of the GCCV-CNN algorithm, experiments are performed on three COVID-19-positive patient datasets and it is compared with existing algorithms. The results show that the classification performance of the algorithm is better than the COVID-Net algorithm and the DeTraC-Net algorithm. The GCCV-CNN algorithm achieves a high accuracy of 98.06%, which is faster and more robust.

Key words: chest X-ray (CXR) images, CT scan images, COVID-19, Grad-CAM, algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN)

摘要：

新型冠状病毒肺炎（COVID-19）检测中胸部X射线（CXR图像）和电子计算机断层扫描（CT）图像是两种主要技术手段,为医生诊断提供了重要依据。针对当前卷积神经网络（CNN）在医学放射性图像中检测COVID-19的准确率不高、算法复杂、无法标记特征区域的问题,提出了一种融合梯度加权类激活映射（Grad-CAM）颜色可视化和卷积神经网络的算法（GCCV-CNN）,对COVID-19阳性患者、COVID-19阴性患者、普通肺炎患者以及正常人的肺部CXR图像和CT扫描图像进行快速分类。通过定位到CXR图像和CT扫描图像中CNN进行分类的关键区域,再综合深度学习算法得到更准确的检测结果。为验证GCCV-CNN算法的有效性,分别在3个COVID-19阳性患者数据集上进行实验,并与已有算法进行比较。结果表明该算法对COVID-19阳性患者的CXR图像和CT扫描图像分类性能优于“新冠网络”（COVID-Net）算法及迁移学习新冠网络（DeTraC-Net）算法,准确率最高达98.06%,速度更快的同时还具有较好的鲁棒性。

关键词: CXR图像, CT扫描图像, COVID-19, Grad-CAM, 融合Grad-CAM颜色可视化和CNN的算法（GCCV-CNN）