CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images.
Hang YangLiyang WangYitian XuXuhua LiuPublished in: International journal of machine learning and cybernetics (2022)
Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.
Keyphrases
- coronavirus disease
- sars cov
- deep learning
- public health
- respiratory syndrome coronavirus
- machine learning
- end stage renal disease
- high resolution
- working memory
- computed tomography
- physical activity
- artificial intelligence
- magnetic resonance imaging
- chronic kidney disease
- intensive care unit
- neural network
- magnetic resonance
- prognostic factors
- patient reported