COVID-19 Identification System from X-Ray Images of Chest using Deep Neural Network with Transfer Learning

Authors

  • Farhana Akter Sunny Department of CSE, Green University of Bangladesh, Dhaka, Bangladesh
  • Sagufta Sabah Nakshi Department of CSE, Green University of Bangladesh, Dhaka, Bangladesh
  • Mashud Parbhez Department of CSE, Green University of Bangladesh, Dhaka, Bangladesh
  • Muhammad Aminur Rahaman Department of CSE, Green University of Bangladesh, Dhaka, Bangladesh

DOI:

https://doi.org/10.3329/gubjse.v10i1.74945

Keywords:

Deep Neural Network, COVID-19 detection, machine learning, transfer learning, Image Dataset

Abstract

Recently, the impact of COVID-19 has significantly diminished; however, it has not been completely eradicated. There are still instances where individuals are experiencing suffering due to this life-threatening virus which has a significant impact on health care as well as lifestyles throughout the world. So, early discovery is important to controlling case extension and the death rate. The RT-PCR is known as the true leading diagnosis test; nevertheless, the expense and result times of these tests are long, thus additional quick and accessible diagnostic techniques are required. However, most countries are suffering due to limited testing resources and kits. The unavailability of testing resources, kits, and a rising amount of regular occurrences, caused us to develop a model on Deep Learning which may benefit radiologists as well as doctors for detecting COVID-19 instances using images of chest X-rays. For developing a representation of modality-specific features, a convolutions neural network and a variety of ImageNet pre-trained models are trained and evaluated at the patient level by using different available CXR datasets. We choose 5000 images in total from the dataset collected from Kaggle where we kept 4000 images in case of training and validation, and the remaining 1000 in case of testing. We use four Pre-train Deep CNN Models which are very popular for image calcification. VVG16, VGG19, InceptionV3, and Resnet50 CNN Models we choose to analyze the performance and find the best one among them. In our testing, we get 88.5% testing accuracy on ResNet and 95.10% on InceptionV3 models while VGG19 gives 90.22% accuracy and VGG16 gives the highest 96.10% accuracy. To increase performance accuracy, Transfer Learning knowledge is transmitted and fine-tuned. After applying Transfer Learning in the modified VGG16 we got an accuracy of 97% which is clearly an improvement over the previous VGG16 model.

GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 53-67

Downloads

Download data is not yet available.
Abstract
104
PDF
72

Downloads

Published

2024-07-15

How to Cite

Farhana Akter Sunny, Nakshi, S. S., Parbhez, M., & Rahaman, M. A. (2024). COVID-19 Identification System from X-Ray Images of Chest using Deep Neural Network with Transfer Learning. GUB Journal of Science and Engineering, 10(1), 53–67. https://doi.org/10.3329/gubjse.v10i1.74945

Issue

Vol. 10 No. 1 (2023)

Section

Articles

License

Copyright (c) 2023 Green University of Bangladesh

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish in the GUB Journal of Science and Engineering agree to the following terms that:

  1. Authors retain copyright and grant the GUB Journal of Science and Engineering the right of first publication of the work.
  2. image
    Articles in GUB Journal of Science and Engineering are licensed under a Creative Commons CC BY-NC-ND License Attribution-NonCommercial-NoDerivatives 4.0 International License. This license permits Share — copy and redistribute the material in any medium or format.

Copyright and Reprint Permissions

  1. Individual contributions contained in it are protected by the copyright of Green University of Bangladesh.
  2. Photocopies of this journal in full or parts for personal or classroom usage may be allowed provided that copies are not made or distributed for profit or commercial advantage and the copies bear this notice and the full citation.
  3. Copyright for components of this work owned by others must be honored. Abstracting with credit is permitted.
  4. Specific permission of the publisher and payment of a fee are required for multiple or systemic copying, advertising or promotional purposes, resale, republishing, posting on servers, redistributing to lists and all forms of document delivery.
  5. Subscribers may reproduce a table of contents or prepare lists of articles including abstracts for internal circulation within their institutions.
  6. Permission of the Publisher is required for resale and distribution outside the institution. Permission of the publisher is required for all other derivative works, including compilations and translations.
  7. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, such as electronic, mechanical, photocopying, magnetic recording or otherwise, without prior written permission of the publisher.
  8. Permissions may be sought directly from the office of the executive editor of GUB Journal of Science and Engineering through E-mail at gubjse@fse.green.edu.bd.

Notice

  • Responsibility for the contents of an article rests upon the author(s) and not upon the editor or the publisher. Therefore, on responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, product instructions or ideas contained in the material herein.