论文标题
Covmunet：从胸部X射线检测Covid-19的多重损失方法
CovMUNET: A Multiple Loss Approach towards Detection of COVID-19 from Chest X-ray
论文作者
论文摘要
Covid-19的最近爆发使整个世界停止了，对公共卫生，全球经济和教育系统产生了毁灭性的影响。由于该病毒的疫苗仍然无法使用，因此对抗病毒的最有效方法是测试和社会疏远。在所有其他检测技术中，基于胸部X射线（CXR）的方法可以是其简单，速度，成本，效率和可访问性的好解决方案。在本文中，我们提出了Covmunet，这是一种多重损失深神网络方法，可从CXR图像中检测COVID-19病例。进行广泛的实验，以确保提出的算法的鲁棒性，并根据精确，召回，准确性和F1得分评估性能。所提出的方法的表现优于最先进的方法，对于3级分类（COVID-19与正常人与肺炎）的精度为96.97％，而2级分类（Covid vs nontoconto）的方法为99.41％。提出的神经结构还成功地检测了CXR图像中的异常。
The recent outbreak of COVID-19 has halted the whole world, bringing a devastating effect on public health, global economy, and educational systems. As the vaccine of the virus is still not available, the most effective way to combat the virus is testing and social distancing. Among all other detection techniques, the Chest X-ray (CXR) based method can be a good solution for its simplicity, rapidity, cost, efficiency, and accessibility. In this paper, we propose CovMUNET, which is a multiple loss deep neural network approach to detect COVID-19 cases from CXR images. Extensive experiments are performed to ensure the robustness of the proposed algorithm and the performance is evaluated in terms of precision, recall, accuracy, and F1-score. The proposed method outperforms the state-of-the-art approaches with an accuracy of 96.97% for 3-class classification (COVID-19 vs normal vs pneumonia) and 99.41% for 2-class classification (COVID vs non-COVID). The proposed neural architecture also successfully detects the abnormality in CXR images.
