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arXiv · 2208.00784

Deep COVID-19 Recognition using Chest X-ray Images: A Comparative Analysis

Selvarajah Thuseethan·Chathrie Wimalasooriya·Shanmuganathan Vasanthapriyan
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Abstract

The novel coronavirus variant, which is also widely known as COVID-19, is currently a common threat to all humans across the world. Effective recognition of COVID-19 using advanced machine learning methods is a timely need. Although many sophisticated approaches have been proposed in the recent past, they still struggle to achieve expected performances in recognizing COVID-19 using chest X-ray images. In addition, the majority of them are involved with the complex pre-processing task, which is often challenging and time-consuming. Meanwhile, deep networks are end-to-end and have shown promising results in image-based recognition tasks during the last decade. Hence, in this work, some widely used state-of-the-art deep networks are evaluated for COVID-19 recognition with chest X-ray images. All the deep networks are evaluated on a publicly available chest X-ray image dataset. The evaluation results show that the deep networks can effectively recognize COVID-19 from chest X-ray images. Further, the comparison results reveal that the EfficientNetB7 network outperformed other existing state-of-the-art techniques.

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Selvarajah Thuseethan, Chathrie Wimalasooriya, Shanmuganathan Vasanthapriyan. 2022-07-26. Deep COVID-19 Recognition using Chest X-ray Images: A Comparative Analysis. https://doi.org/10.1109/slaai-icai54477.2021.9664727

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