Automated COVID-19 Detection with Ensemble Deep Convolution Neural Networks using Computed Tomography Images

Authors

  • Anupam Muralidharanr Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Achyut Shukla Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Antriksh Sharma Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Ojas Inamdar Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Deepak Parashar Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Preksha Pareek Symbiosis Institute of Technology, Symbiosis International Deemed University, Pune- 412115, India
  • Mohammad Farhad Bulbul Department of Mathematics, Jashore University of Science and Technology, Jashore 7408, Bangladesh
  • Daijin Kim Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam, Pohang 37673, Republic of Korea

Keywords:

Convolutional Neural Network, Computed Tomography, COVID-19, Deep Learning, Machine Learning

Abstract

The recently identified presence of the novel coronavirus (COVID-19) has had disastrous effects, and the World Health Organization (WHO) has declared it a serious worldwide pandemic. A person’s contact with the virus must be discovered as soon as possible to begin treatment and quarantine (if necessary) and prevent the virus from spreading to others in good health. This is equally as crucial as identifying the disease’s root cause. In this study, we will investigate the use of various models based on Deep Learning (DL) techniques for the purpose of screening COVID-19, as well as the advantages and drawbacks of these methods in contrast with others. We will look into the potential value of this imaging method for the management and early treatment of COVID-19 patients and review recent research studies that examined the accuracy and reliability of various pre-processing methods and models on chest CT scans for COVID-19 diagnosis.

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Published

25.12.2023

How to Cite

Muralidharanr, A. ., Shukla, A. ., Sharma, A. ., Inamdar, O. ., Parashar, D. ., Pareek, P. ., Bulbul, M. F. ., & Kim, D. . (2023). Automated COVID-19 Detection with Ensemble Deep Convolution Neural Networks using Computed Tomography Images. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 516–527. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3949

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

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