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.
翻译:COVID-19是一种全球性的流行病,检测这些疾病是当今医学专业人员的一项重大任务,因为其突变迅速。目前检查胸部X光和CT扫描的方法需要深刻的知识,而且需要时间,这表明当人命受到威胁时,它缩短了医生的宝贵时间。这项研究试图通过微调视野变异器(View Greener)在胸部X光分类方面实现最先进的表现来协助这一进程。拟议方法使用预先训练的模式,为发现胸部X光中存在COVID-19疾病而进行微调。这种方法的精确率达到97.61%,精准率达到95.34 %,记数达到93.84%,而F1分为94.58%。这一结果表示以变压器为基础的胸部X光模型的性能。