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The COVID-19 pandemic has led to a global health crisis, highlighting theneed for rapid and accurate virus detection. This research paper examinestransfer learning with vision transformers for COVID-19 detection, known forits excellent performance in image recognition tasks. We leverage thecapability of Vision Transformers to capture global context and learn complexpatterns from chest X-ray images. In this work, we explored the recentstate-of-art transformer models to detect Covid-19 using CXR images such asvision transformer (ViT), Swin-transformer, Max vision transformer (MViT), andPyramid Vision transformer (PVT). Through the utilization of transfer learningwith IMAGENET weights, the models achieved an impressive accuracy range of98.75% to 99.5%. Our experiments demonstrate that Vision Transformers achievestate-of-the-art performance in COVID-19 detection, outperforming traditionalmethods and even Convolutional Neural Networks (CNNs). The results highlightthe potential of Vision Transformers as a powerful tool for COVID-19 detection,with implications for improving the efficiency and accuracy of screening anddiagnosis in clinical settings.

Study of Vision Transformers for Covid-19 Detection from Chest X-rays