Vision Transformer based COVID-19 Detection using Chest X-rays

TL;DR

This paper presents a Vision Transformer-based method for detecting COVID-19 from chest X-rays, achieving high accuracy and efficiency to assist medical diagnosis.

Contribution

It introduces a fine-tuned Vision Transformer approach using pretrained models for COVID-19 detection on chest X-rays, demonstrating state-of-the-art performance.

Findings

Accuracy of 97.61% in COVID-19 detection

High precision and recall scores

Transformer models outperform traditional methods

Abstract

COVID-19 is a global pandemic, and detecting them is a momentous task for medical professionals today due to its rapid mutations. Current methods of examining chest X-rays and CT scan requires profound knowledge and are time consuming, which suggests that it shrinks the precious time of medical practitioners when people's lives are at stake. This study tries to assist this process by achieving state-of-the-art performance in classifying chest X-rays by fine-tuning Vision Transformer(ViT). The proposed approach uses pretrained models, fine-tuned for detecting the presence of COVID-19 disease on chest X-rays. This approach achieves an accuracy score of 97.61%, precision score of 95.34%, recall score of 93.84% and, f1-score of 94.58%. This result signifies the performance of transformer-based models on chest X-ray.