The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

The Performance Evaluation of Transfer Learning VGG16 Algorithm on Various Chest X-ray Imaging Datasets for COVID-19 Classification

Author 1: Andi Sunyoto
Author 2: Yoga Pristyanto
Author 3: Arief Setyanto
Author 4: Fawaz Alarfaj
Author 5: Naif Almusallam
Author 6: Mohammed Alreshoodi

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130923

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Early detection of the coronavirus (COVID-19) disease is essential in order to contain the spread of the virus and provide effective treatment. Chest X-rays could be used to detect COVID-19 at an early stage. However, the pathological features of COVID-19 on chest X-rays closely resemble those caused by other viruses. The visual geometry group-16 (VGG16) deep learning algorithm based on convolutional neural network (CNN) architecture is commonly used to detect various pathologies on medical images automatically and may have a role in the detection of COVID-19 on chest X-rays. Therefore, this research is aimed to determine the robustness of the VGG16 architecture on several chest X-ray databases that vary in terms of size and the number of class labels. Nine publicly available chest X-ray datasets were used to train and test the algorithm. Each dataset had a different number of images, class compositions, and interclass proportions. The performance of the architecture was tested using several scenarios, including datasets above and below 5,000 samples, label class variation, and interclass ratio. This study confirmed that the VGG16 delivers robust performance on various datasets, achieving an accuracy of up to 97.99%. However, our findings also suggest that the accuracy of the VGG16 algorithm drops drastically in highly imbalanced datasets.

Keywords: Covid-19; Chest X-Ray; CNN; transfer learning; VGG-16

Andi Sunyoto, Yoga Pristyanto, Arief Setyanto, Fawaz Alarfaj, Naif Almusallam and Mohammed Alreshoodi, “The Performance Evaluation of Transfer Learning VGG16 Algorithm on Various Chest X-ray Imaging Datasets for COVID-19 Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130923

@article{Sunyoto2022,
title = {The Performance Evaluation of Transfer Learning VGG16 Algorithm on Various Chest X-ray Imaging Datasets for COVID-19 Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130923},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130923},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Andi Sunyoto and Yoga Pristyanto and Arief Setyanto and Fawaz Alarfaj and Naif Almusallam and Mohammed Alreshoodi}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org