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 two-class and three-class classifications, 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 15\(\times \) 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 is available.
Md. Mohaiminul Islam and Tanveer Hannan are contributed equally and share the first-authorship of this paper.
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Notes
- 1.
Code for reproducing results is available at https://github.com/mmiemon/COVID-DenseNet, and models’ weights can be found at https://bit.ly/2YZwyk3.
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Islam, M.M., Hannan, T., Sarker, L., Ahmed, Z. (2023). COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_28
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