Open Access iconOpen Access

ARTICLE

crossmark

COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network

Jiaji Wang1,#, Shuwen Chen1,2,3,#,*, Yu Cao1,#, Huisheng Zhu1, Dimas Lima4,*

1 School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China
2 State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, 210096, China
3 Jiangsu Province Engineering Research Center of Basic Education Big Data Application, Nanjing, 211200, China
4 Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, 88040-900, Brazil

* Corresponding Authors: Shuwen Chen. Email: email; Dimas Lima. Email: email

(This article belongs to this Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)

Computer Modeling in Engineering & Sciences 2023, 136(3), 2595-2616. https://doi.org/10.32604/cmes.2023.025804

Abstract

This paper presents a 6-layer customized convolutional neural network model (6L-CNN) to rapidly screen out patients with COVID-19 infection in chest CT images. This model can effectively detect whether the target CT image contains images of pneumonia lesions. In this method, 6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a sample. The results show that the model improves the accuracy of screening out COVID-19 patients. Compared to other methods, the performance is better. In addition, the method can be extended to other similar clinical conditions.

Keywords


Cite This Article

Wang, J., Chen, S., Cao, Y., Zhu, H., Lima, D. (2023). COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network. CMES-Computer Modeling in Engineering & Sciences, 136(3), 2595–2616.



cc 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.
  • 730

    View

  • 733

    Download

  • 0

    Like

Share Link