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

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Md Mohaiminul Islam^*,Tanveer Hannan,Laboni Sarker,Zakaria Ahmed

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Preprints on COVID-19 and SARS-CoV-2

Submitted:

12 February 2022

Posted:

18 February 2022

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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.

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Subject: Computer Science and Mathematics - Information Systems

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