Sandeep Angara* and Sharath Thirunagaru

Department of Computer Sciences, USA

*Corresponding Author: Sandeep Angara, Department of Computer Sciences, USA

Received: July 06, 2023; Published: July 21, 2023

Abstract

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-ofthe-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.

Keywords: Vision Transformers; Swin-Transformers; Covid-19; Chest X-rays

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Citation

Citation: Sandeep Angara and Sharath Thirunagaru. “Study of Vision Transformers for Covid-19 Detection from Chest X-rays".Acta Scientific Computer Sciences 5.8 (2023): 17-25.

Copyright

Copyright: © 2023 Sandeep Angara and Sharath Thirunagaru. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.