Version 1
: Received: 7 May 2020 / Approved: 9 May 2020 / Online: 9 May 2020 (04:54:34 CEST)
Version 2
: Received: 14 January 2021 / Approved: 15 January 2021 / Online: 15 January 2021 (12:59:20 CET)
Version 3
: Received: 12 February 2022 / Approved: 18 February 2022 / Online: 18 February 2022 (14:44:55 CET)
How to cite:
Islam, M.M.; Hannan, T.; Sarker, L.; Ahmed, Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Preprints2020, 2020050151. https://doi.org/10.20944/preprints202005.0151.v3.
Islam, M.M.; Hannan, T.; Sarker, L.; Ahmed, Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Preprints 2020, 2020050151. https://doi.org/10.20944/preprints202005.0151.v3.
Cite as:
Islam, M.M.; Hannan, T.; Sarker, L.; Ahmed, Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Preprints2020, 2020050151. https://doi.org/10.20944/preprints202005.0151.v3.
Islam, M.M.; Hannan, T.; Sarker, L.; Ahmed, Z. COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. Preprints 2020, 2020050151. https://doi.org/10.20944/preprints202005.0151.v3.
Abstract
COVID-19 has a severe risk of spreading rapidly, the quick identification of which is essential. In this regard, chest radiology images have proven to be a practical screening approach for COVID-19 affected patients. This study proposes a deep learning-based approach using Densenet-121 to detect COVID-19 patients effectively. We have trained and tested our model on the COVIDx dataset and performed both 2-class and 3-class classification, achieving 96.49% and 93.71% accuracy, respectively. By successfully utilizing transfer learning, we achieve comparable performance to the state-of-the-art method while using 15x fewer model parameters. Moreover, we performed an interpretability analysis using Grad-CAM to highlight the most significant image regions at test time. Finally, we developed a website that takes chest radiology images as input and detects the presence of COVID-19 or pneumonia and a heatmap highlighting the infected regions. Source code for reproducing results and model weights are available.
Keywords
deep learning; CNN; DenseNet; COVID-19; transfer learning
Subject
MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Md. Mohaiminul Islam
Commenter's Conflict of Interests: Author
2. Updated introduction.
3. Added new related works.
4. Updated some experiments.