Deep COVID-19 Recognition using Chest X-ray Images: A Comparative Analysis
| Autor: | Thuseethan, Selvarajah, Wimalasooriya, Chathrie, Vasanthapriyan, Shanmuganathan |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | In 2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/SLAAI-ICAI54477.2021.9664727 |
| Popis: | 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. Comment: 5 pages |
| Databáze: | arXiv |
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