Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

目前，COVID-19 从检测到治疗对全球研究人员、科学家、卫生专业人员和管理部门构成了严重威胁。由于 COVID-19 大流行，全世界都在经历类似封锁的情况。研究人员正在不懈努力，寻求在各自领域控制这一流行病的可能解决方案。研究人员应用的最常见、最有效的方法之一是使用 CT 扫描和 X 射线来分析肺部图像中的 COVID-19。然而，它需要多名放射学专家和时间来手动检查每份报告，这是大流行中具有挑战性的任务之一。在本文中，我们提出了一种基于深度学习神经网络的方法 nCOVnet，这是一种替代的快速筛查方法，可通过分析患者的 X 射线来寻找胸部中发现的视觉指标来检测 COVID-19 COVID-19 患者的放射线成像。