One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.

在有症状的患者中早期检测 2019 年冠状病毒病 (COVID-19) 的有希望的方法之一是使用深度学习 (DL) 技术分析个人的胸部计算机断层扫描 (CT) 扫描或胸部 X 射线图像。本文提出了一种新颖的堆叠集成，可以从个人的胸部 CT 扫描或胸部 X 射线图像中检测 COVID-19。所提出的模型是异构的预训练计算机视觉模型的堆叠集合。考虑了四个预训练的 DL 模型：视觉几何组 (VGG 19)、残差网络 (ResNet 101)、密集连接卷积网络 (DenseNet 169) 和宽残差网络 (WideResNet 50 2)。从每个预训练模型中，通过改变额外的全连接层的数量来获得基本分类器的潜在候选者。经过一番苦苦寻觅，选择了三个表现最佳的不同模型来设计基于加权平均的异构堆叠集成。使用五种不同的胸部 CT 扫描和胸部 X 射线图像来训练和评估所提出的模型。将所提出模型的性能与其他两个集成模型、基线预训练计算机视觉模型和现有的 COVID-19 检测模型进行了比较。所提出的模型在五个不同的数据集上取得了一致的良好性能，包括胸部 CT 扫描和胸部 X 射线图像。与 COVID-19 相关，由于召回比精度更重要，因此探索了不同阈值下的召回和精度之间的权衡。为每个数据集获得了产生高召回率和准确性的推荐阈值。