The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning–based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning–based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.

新发现的疾病冠状病毒俗称 COVID-19，是由严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 引起的，并被世界卫生组织 (WHO) 宣布为大流行病。COVID-19 的早期检测对于遏制其引起的大流行至关重要。在这项研究中，提出了一种基于迁移学习的 COVID-19 筛查技术。这项研究的动机是设计一个自动化系统，可以帮助医务人员，特别是在训练有素的员工人数众多的地区。该研究调查了基于迁移学习的模型自动诊断 COVID-19 等疾病以协助医疗力量的潜力，尤其是在疫情爆发时。在提议的工作中，一个深度学习模型，即 截断 VGG16（牛津视觉几何组）用于筛查 COVID-19 CT 扫描。VGG16 架构经过微调，用于从 CT 扫描图像中提取特征。进一步的主成分分析（PCA）用于特征选择。对于最终分类，比较了四种不同的分类器，即深度卷积神经网络 (DCNN)、极限学习机 (ELM)、在线顺序 ELM 和带有支持向量机 (SVM) 的装袋集成。在 385 ms 内使用 SVM 的最佳分类器 bagging 集成在 208 个测试图像上实现了 95.7% 的准确度、95.8% 的精度、0.958 的曲线下面积 (AUC) 和 95.3% 的 F1 分数。在不同数据集上获得的结果证明了所提出工作的优越性和稳健性。还提出了一种用于放射学数据的预处理技术。该研究进一步将预训练的 CNN 架构和分类模型与所提出的技术进行比较。