Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

2019 年冠状病毒病 (COVID-19) 是由新型冠状病毒引起的流行病。COVID-19 正在世界范围内迅速传播。诊断 COVID-19 的黄金标准是逆转录聚合酶链反应 (RT-PCR) 测试。然而，RT-PCR检测设施有限，导致该病的早期诊断困难。X 射线等容易获得的方法可用于检测与 COVID-19 相关的特定症状。预训练的卷积神经网络广泛用于从较小的数据集中对疾病进行计算机辅助检测。本文研究了多 CNN（多个预训练 CNN 的组合）在从 X 射线图像中自动检测 COVID-19 方面的有效性。该方法将从多 CNN 中提取的特征与基于相关的特征选择 (CFS) 技术和贝叶斯网分类器相结合来预测 COVID-19。该方法使用两个公共数据集进行了测试，并在两个数据集上取得了有希望的结果。在由 453 张 COVID-19 图像和 497 张非 COVID 图像组成的第一个数据集中，该方法的 AUC 为 0.963，准确率为 91.16%。在由 71 张 COVID-19 图像和 7 张非 COVID 图像组成的第二个数据集中，该方法的 AUC 为 0.911，准确率为 97.44%。本研究中进行的实验证明了预训练的多 CNN 在检测 COVID-19 方面比单 CNN 更有效。