COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

COVID-19（也称为 SARS-COV-2）大流行已在全世界蔓延。它是一种传染性疾病，很容易从一个直接接触的人传播到另一个人，专家将其分为五类：无症状、轻度、中度、重度和危重。截至 2020 年 12 月 5 日，全球已有超过 6600 万人受到感染，活跃患者超过 2200 万，而且这一速度正在加快。全世界有超过 150 万患者（约占报告病例总数的 2.5%）丧生。在许多地方，COVID-19 检测是通过可能需要超过 48 小时的逆转录聚合酶链反应 (RT-PCR) 测试进行的。这是其严重性和迅速传播的主要原因之一。我们在本文中提出了一个两阶段X-称为 XCOVNet 的射线图像分类，用于使用卷积神经网络模型进行早期COV ID-19 检测。XCOVNet 分两个阶段检测胸部 X 光患者图像中的 COVID-19 感染。第一阶段预处理包含 392 张胸部 X 光图像的数据集，其中一半为 COVID-19 阳性，一半为阴性。第二阶段训练和调整神经网络模型，以达到 98.44% 的患者分类准确率。