Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

中文翻译：

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使用基于多目标差分进化的卷积神经网络从胸部 CT 图像中对 COVID-19 患者进行分类。

2019 年新型冠状病毒病 (COVID-19) 的早期分类对于疾病的治愈和控制至关重要。与逆转录聚合酶链反应 (RT-PCR) 相比，胸部计算机断层扫描 (CT) 成像可能是一种更可靠、更有用和更快速的 COVID-19 分类和评估技术，特别是在流行地区。几乎所有医院都有CT成像机；因此，胸部 CT 图像可用于 COVID-19 患者的早期分类。然而，基于胸部 CT 的 COVID-19 分类涉及放射科专家和相当长的时间，这在 COVID-19 感染快速增长时非常有价值。因此，需要对胸部 CT 图像进行自动分析，以节省医疗专业人员的宝贵时间。在本文中，卷积神经网络 (CNN) 用于将感染 COVID-19 的患者分类为感染 (+ve) 或未感染 (-ve)。此外，CNN 的初始参数使用多目标差分进化 (MODE) 进行调整。通过考虑胸部 CT 图像上提出的和竞争性的机器学习技术，进行了广泛的实验。广泛的分析表明，所提出的模型可以以良好的准确率对胸部 CT 图像进行分类。