Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.

文献调查表明，基于卷积神经网络 (CNN) 的预训练模型已广泛用于使用胸部 X 射线 (CXR) 和计算机断层扫描 (CT) 数据集对 2019 年冠状病毒病 (COVID-19) 进行分类。然而，大多数方法都使用较少数量的数据样本用于 CT 和 CXR 数据集进行训练、验证和测试。因此，该模型可能在测试期间表现出良好的性能，但这种类型的模型在未见过的 COVID-19 数据样本上不会更有效。泛化是设计分类器时要考虑的一个重要术语，该分类器可以在完全看不见的数据集上表现良好。在这里，这项工作提出了一种用于 COVID-19 分类的具有堆叠集成元分类器和基于深度学习的特征融合方法的大规模学习。提取基于 EfficientNet 的预训练模型倒数第二层（全局平均池化）的特征，并使用核主成分分析 (PCA) 降低提取特征的维数。接下来，采用特征融合方法来融合各种提取特征的特征。最后，使用基于堆叠集成元分类器的方法进行分类。这是一个两阶段的方法。在第一阶段，应用随机森林和支持向量机（SVM）进行预测，然后聚合并输入第二阶段。第二阶段包括逻辑回归分类器，将 CT 和 CXR 的数据样本分类为 COVID-19 或非 COVID-19。所提出的模型使用公开的大型 CT 和 CXR 数据集进行了测试。将所提出模型的性能与各种现有的基于 CNN 的预训练模型进行了比较。所提出的模型优于现有方法，可用作医疗保健专业人员进行即时诊断的工具。