TY  - EJOU
AU  - Hou, Shouming 
AU  - Han, Ji 

TI  - COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2022
VL  - 130
IS  - 2
SN  - 1526-1506

AB  - Many people around the world have lost their lives due to COVID-19. The symptoms of most COVID-19 patients are fever, tiredness and dry cough, and the disease can easily spread to those around them. If the infected people can be detected early, this will help local authorities control the speed of the virus, and the infected can also be treated in time. We proposed a six-layer convolutional neural network combined with max pooling, batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients. In the 10-fold cross-validation methods, our method is superior to several state-of-the-art methods. In addition, we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection.
KW  - COVID-19; deep learning; convolutional neural network; max pooling; batch normalization; Adam;  Grad-CAM

DO  - 10.32604/cmes.2022.016621