There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 +ve) and uninfected (COVID-19 −ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

中文翻译：

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使用胸部 X 射线图像对 COVID-19 感染患者进行基于深度神经网络的筛查模型

2019 年冠状病毒病 (COVID-19) 检测试剂盒数量有限，因此，需要开发其他诊断方法。医生通常使用胸部 X 射线和计算机断层扫描 (CT) 扫描来诊断肺炎、肺部炎症、脓肿和/或淋巴结肿大。由于 COVID-19 攻击我们呼吸道的上皮细胞，因此，本文使用 X 射线图像对感染患者（COVID-19+ve) 和未感染 (COVID-19-五）肺。几乎所有医院都有 X 射线成像机，因此，胸部 X 射线图像可用于检测 COVID-19，而无需使用任何类型的专用测试套件。然而，基于胸部 X 射线的 COVID-19 分类需要放射学专家和大量时间，这在 COVID-19 感染快速增加时非常宝贵。因此，需要开发一种自动分析方法来节省医疗专业人员的宝贵时间。在本文中，设计和实现了一种深度卷积神经网络（CNN）方法。此外，CNN 的超参数使用多目标自适应差分进化 (MADE) 进行调整。通过考虑基准 COVID-19 数据集进行了广泛的实验。