Vision Transformer based COVID-19 Detection using Chest X-rays
This work addresses the problem of time-consuming COVID-19 diagnosis for medical professionals, but it is incremental as it applies an existing method (ViT) to a specific medical dataset.
The study tackled COVID-19 detection from chest X-rays by fine-tuning a Vision Transformer (ViT) model, achieving state-of-the-art performance with an accuracy of 97.61%, precision of 95.34%, recall of 93.84%, and F1-score of 94.58%.
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.