COVID-19 Detection Algorithm Combining Grad-CAM and Convolutional Neural Network

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.

Keywords

• |chest x-ray (cxr) images|ct scan images|covid-19|grad-cam|algorithm combining grad-cam color visualization and convolutional neural network (gccv-cnn)