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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- 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 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
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