In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease.

在医院，由于每天病例激增，可用的 COVID-19 检测试剂盒数量有限。出于这个原因，应该引入一种快速替代诊断选项作为一种自动检测方法，以防止 COVID-19 在个体之间传播。本文提出了多目标优化和深度学习方法，用于用 X 射线检测感染的冠状病毒患者。J48决策树方法对受影响的X射线电晕图像的深层特征进行分类，以有效检测受污染的患者。本研究开发了 11 种不同的基于卷积神经元网络 (CNN) 的模型，用于使用 X 射线图像（AlexNet、VGG16、VGG19、GoogleNet、ResNet18、ResNet500、ResNet101、InceptionV3、InceptionResNetV2、DenseNet201 和 XceptionNet）检测冠状病毒肺炎感染患者）。此外，CNN深度学习模型的参数使用具有多个目标的帝企鹅优化器（MOEPO）来描述。广泛的审查表明，所提出的模型可以以正确的精度、准确度、召回率、特异性和 F1 分数对 X 射线图像进行分类。广泛的测试结果表明，所提出的模型优于具有众所周知的效率指标的竞争模型。因此，所提出的模型对于 COVID-19 疾病的 X 射线胸部图像的实时分类很有用。广泛的测试结果表明，所提出的模型优于具有众所周知的效率指标的竞争模型。因此，所提出的模型对于 COVID-19 疾病的 X 射线胸部图像的实时分类很有用。广泛的测试结果表明，所提出的模型优于具有众所周知的效率指标的竞争模型。因此，所提出的模型对于 COVID-19 疾病的 X 射线胸部图像的实时分类很有用。