  The COVID-19 pandemic has led to a global health crisis, highlighting the
need for rapid and accurate virus detection. This research paper examines
transfer learning with vision transformers for COVID-19 detection, known for
its excellent performance in image recognition tasks. We leverage the
capability of Vision Transformers to capture global context and learn complex
patterns from chest X-ray images. In this work, we explored the recent
state-of-art transformer models to detect Covid-19 using CXR images such as
vision transformer (ViT), Swin-transformer, Max vision transformer (MViT), and
Pyramid Vision transformer (PVT). Through the utilization of transfer learning
with IMAGENET weights, the models achieved an impressive accuracy range of
98.75% to 99.5%. Our experiments demonstrate that Vision Transformers achieve
state-of-the-art performance in COVID-19 detection, outperforming traditional
methods and even Convolutional Neural Networks (CNNs). The results highlight
the potential of Vision Transformers as a powerful tool for COVID-19 detection,
with implications for improving the efficiency and accuracy of screening and
diagnosis in clinical settings.
