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ORIGINAL RESEARCH article

Front. Signal Process.
Sec. Biomedical Signal Processing
Volume 4 - 2024 | doi: 10.3389/frsip.2024.1384744
This article is part of the Research Topic Advances in Biomedical Image Segmentation and Analysis using Deep Learning View all 5 articles

CovRoot: COVID-19 Detection based on Chest Radiology Imaging Techniques using Deep Learning

Provisionally accepted
Ahashan Habib Niloy Ahashan Habib Niloy 1S.M. Farah Al Fahim S.M. Farah Al Fahim 1Mohammad Zavid Parvez Mohammad Zavid Parvez 2*Shammi Akhter Shiba Shammi Akhter Shiba 1Faizun Nahar Faria Faizun Nahar Faria 1Md. Jamilur Rahman Md. Jamilur Rahman 1Emtiaz Hussain Emtiaz Hussain 1Tasmi Tamanna Tasmi Tamanna 3
  • 1 Department of Computer Science and Engineering, BRAC University, Dhaka, Dhaka, Bangladesh
  • 2 School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, Australia
  • 3 Institute of Health and Sport, College of Health and Biomedicine, Victoria University, Melbourne, Victoria, Australia

The final, formatted version of the article will be published soon.

    The world first came to know the existence of COVID-19 (SARS-CoV-2) in December 2019. Initially, doctors struggled to diagnose the increasing number of patients due to less availability of testing kits. To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN). Previously some research methods were based on X-ray images and others on CT scan images. Few research methods addressed both image types, with the proposed models limited to detecting only COVID and NORMAL cases. This limitation motivated us to propose a 42-layer CNN model that works for complex scenarios (COVID, NORMAL, and PNEUMONIA VIRAL) and more complex scenarios (COVID, NORMAL, PNEUMONIA VIRAL, and PNEUMONIA BACTERIA). Furthermore, our proposed model indicates better performance than any other previously proposed models in the detection of COVID-19.

    Keywords: Convolutional Neural Network, deep learning, COVID-19, x-ray, CT scan

    Received: 10 Feb 2024; Accepted: 06 Aug 2024.

    Copyright: © 2024 Niloy, Fahim, Parvez, Shiba, Faria, Rahman, Hussain and Tamanna. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mohammad Zavid Parvez, School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, Australia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.