Prajoy Podder
Rethinking densely connected convolutional networks for diagnosing infectious diseases.
Podder, Prajoy; Alam, Fatema Binte; Mondal, M. Rubaiyat Hossain; Hasan, Md Junayed; Rohan, Ali; Bharati, Subrato
Authors
Fatema Binte Alam
M. Rubaiyat Hossain Mondal
Dr Md Junayed Hasan j.hasan@rgu.ac.uk
Research Fellow A
Dr Ali Rohan a.rohan@rgu.ac.uk
Research Fellow
Subrato Bharati
Abstract
Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The mode'ls ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.
Citation
PODDER, P., ALAM, F.B., MONDAL, M.R.H., HASAN, M.J., ROHAN, A. and BHARATI, S. 2023. Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers [online], 12(5), article 95. Available from: https://doi.org/10.3390/computers12050095
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 28, 2023 |
Online Publication Date | May 2, 2023 |
Publication Date | May 1, 2023 |
Deposit Date | May 7, 2023 |
Publicly Available Date | Jun 8, 2023 |
Journal | Computers |
Electronic ISSN | 2073-431X |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 5 |
Article Number | 95 |
DOI | https://doi.org/10.3390/computers12050095 |
Keywords | Convolutional neural network; Deep learning; COVID-19; DenseNet 169; DenseNet 201; Transfer learning |
Public URL | https://rgu-repository.worktribe.com/output/1953142 |
Files
PODDER 2023 Rethinking densely connected (VOR)
(2.8 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2023 by the authors. Licensee MDPI, Basel, Switzerland.
You might also like
Vision based relative position estimation in surgical robotics.
(2023)
Conference Proceeding
Tracking and estimation of surgical instrument position and angle in surgical robot using vision system.
(2023)
Conference Proceeding
Person recognition based on deep gait: a survey.
(2023)
Journal Article
Data-driven solution to identify sentiments from online drug reviews.
(2023)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search