Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed

COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

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. Preprints 2020, 2020050151 (doi: 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 (doi: 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

Comments (1)

Comment 1
Received: 18 February 2022
Commenter: Md. Mohaiminul Islam
Commenter's Conflict of Interests: Author
Comment: 1. Updated abstract.
2. Updated introduction.
3. Added new related works.
4. Updated some experiments.
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