Open Access

ARTICLE

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 the 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. https://doi.org/10.32604/cmes.2022.016621

Received 12 March 2021; Accepted 05 August 2021; Issue published 13 December 2021

Abstract

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.

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Keywords

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