{ "id": "2307.09402", "version": "v1", "published": "2023-07-17T14:06:07.000Z", "updated": "2023-07-17T14:06:07.000Z", "title": "Study of Vision Transformers for Covid-19 Detection from Chest X-rays", "authors": [ "Sandeep Angara", "Sharath Thirunagaru" ], "categories": [ "eess.IV", "cs.CV" ], "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-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.", "revisions": [ { "version": "v1", "updated": "2023-07-17T14:06:07.000Z" } ], "analyses": { "keywords": [ "chest x-ray", "paper examines transfer learning", "vision transformers achieve state-of-the-art performance", "research paper examines transfer" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }