Chest CT is used in the COVID-19 diagnosis process as a significant complement to the reverse transcription polymerase chain reaction (RT–PCR) technique. However, it has several drawbacks, including long disinfection and ventilation times, excessive radiation effects, and high costs. While X-ray radiography is more useful for detecting COVID-19, it is insensitive to the early stages of the disease. We have developed inference engines that will turn X-ray machines into powerful diagnostic tools by using deep learning technology to detect COVID-19. We named these engines COV19-CNNet and COV19-ResNet. The former is based on convolutional neural network architecture; the latter is on residual neural network (ResNet) architecture. This research is a retrospective study. The database consists of 210 COVID-19, 350 viral pneumonia, and 350 normal (healthy) chest X-ray (CXR) images that were created using two different data sources. This study was focused on the problem of multi-class classification (COVID-19, viral pneumonia, and normal), which is a rather difficult task for the diagnosis of COVID-19. The classification accuracy levels for COV19-ResNet and COV19-CNNet were 97.61% and 94.28%, respectively. The inference engines were developed from scratch using new and special deep neural networks without pre-trained models, unlike other studies in the field. These powerful diagnostic engines allow for the early detection of COVID-19 as well as distinguish it from viral pneumonia with similar radiological appearances. Thus, they can help in fast recovery at the early stages, prevent the COVID-19 outbreak from spreading, and contribute to reducing pressure on health-care systems worldwide.

胸部 CT 用于 COVID-19 诊断过程，作为逆转录聚合酶链反应 (RT-PCR) 技术的重要补充。然而，它有几个缺点，包括消毒和通风时间长、辐射效应过大和成本高。虽然 X 射线照相对于检测 COVID-19 更有用，但它对疾病的早期阶段不敏感。我们开发了推理引擎，通过使用深度学习技术检测 COVID-19，将 X 射线机变成强大的诊断工具。我们将这些引擎命名为 COV19-CNNNet 和 COV19-ResNet。前者基于卷积神经网络架构；后者是关于残差神经网络（ResNet）架构的。本研究为回顾性研究。该数据库包含 210 个 COVID-19、350 个病毒性肺炎、以及使用两个不同数据源创建的 350 张正常（健​​康）胸部 X 光 (CXR) 图像。本研究的重点是多分类问题（COVID-19、病毒性肺炎和正常），这对于 COVID-19 的诊断来说是一项相当艰巨的任务。COV19-ResNet 和 COV19-CNNNet 的分类准确率分别为 97.61% 和 94.28%。与该领域的其他研究不同，推理引擎是使用新的和特殊的深度神经网络从头开发的，没有预先训练的模型。这些强大的诊断引擎可以早期发现 COVID-19，并将其与具有相似放射学表现的病毒性肺炎区分开来。因此，它们可以帮助在早期阶段快速恢复，防止 COVID-19 爆发蔓延，