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COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network

Shouming Hou, Ji Han*

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

* Corresponding Author: Ji Han. Email:

(This article belongs to this Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)

Computer Modeling in Engineering & Sciences 2022, 130(2), 855-869.


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.


Cite This Article

Hou, S., Han, J. (2022). COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network. CMES-Computer Modeling in Engineering & Sciences, 130(2), 855–869.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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