The infectious disease caused by novel corona virus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized corona virus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods.

翻译：自去年以来，由新型冠状病毒（2019-nCoV）引起的传染病广泛传播并已波及全球。它对日常生活、全球经济及公共卫生造成了前所未有的影响。因此，该疾病的检测对患者和医生均具有挽救生命的重要意义。由于检测试剂盒数量有限，使用传统技术（RT-PCR）对每位患有严重呼吸道问题的患者进行检测亦是一项艰巨任务。因此，迫切需要建立自动诊断系统以缓解医院及医疗保健系统中COVID-19检测试剂盒短缺的问题。诊断方法主要分为两类：基于实验室的方法和胸部放射学方法。本文提出了一种基于肺部X光图像的计算机化新型冠状病毒（2019-nCoV）检测新方法。我们利用深度学习构建模型，以展示诊断系统的有效性。在实验结果中，我们在公开数据集上评估了所提出的模型，与其他现有方法相比，其表现出令人满意的性能与前景良好的结果。