Since the onset of 2020, the spread of coronavirus disease (COVID-19) has rapidly accelerated worldwide into a state of severe pandemic. COVID-19 has infected more than 29 million people and caused more than 900 thousand deaths at the time of writing. Since it is highly contagious, it causes explosive community transmission. Thus, health care delivery has been disrupted and compromised by the lack of testing kits. COVID-19-infected patients show severe acute respiratory syndrome. Meanwhile, the scientific community has been involved in the implementation of deep learning (DL) techniques to diagnose COVID-19 using computed tomography (CT) lung scans, since CT is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. However, large datasets of CT-scan images are not publicly available due to privacy concerns and obtaining very accurate models has become difficult. Thus, to overcome this drawback, transfer-learning pre-trained models are used in the proposed methodology to classify COVID-19 (positive) and COVID-19 (negative) patients. We describe the development of a DL framework that includes pre-trained models (DenseNet201, VGG16, ResNet50V2, and MobileNet) as its backbone, known as KarNet. To extensively test and analyze the framework, each model was trained on original (i.e. unaugmented) and manipulated (i.e. augmented) datasets. Among the four pre-trained models of KarNet, the one that used DenseNet201 demonstrated excellent diagnostic ability, with AUC scores of 1.00 and 0.99 for models trained on unaugmented and augmented data sets, respectively. Even after considerable distortion of the images (i.e. the augmented dataset) DenseNet201 achieved an accuracy of 97% for the test dataset, followed by ResNet50V2, MobileNet, and VGG16 (which achieved accuracies of 96%, 95%, and 94%, respectively).

自 2020 年开始，冠状病毒病 (COVID-19) 的传播在全球范围内迅速加速，进入严重大流行状态。在撰写本文时，COVID-19 已感染超过 2900 万人并导致超过 90 万人死亡。由于它具有高度传染性，因此会导致爆炸性的社区传播。因此，由于缺乏检测试剂盒，医疗保健服务受到干扰和影响。COVID-19 感染患者表现出严重的急性呼吸系统综合症。同时，科学界已参与实施深度学习 (DL) 技术以使用计算机断层扫描 (CT) 肺扫描诊断 COVID-19，因为 CT 是一种相关的筛查工具，因为它在识别早期肺炎变化方面具有更高的敏感性。然而，由于隐私问题，CT 扫描图像的大型数据集不公开，获得非常准确的模型变得困难。因此，为了克服这个缺点，在所提出的方法中使用了迁移学习预训练模型来对 COVID-19（阳性）和 COVID-19（阴性）患者进行分类。我们描述了 DL 框架的开发，该框架包括预训练模型（DenseNet201、VGG16、ResNet50V2 和 MobileNet）作为其主干，称为 KarNet。为了广泛测试和分析框架，每个模型都在原始（即未增强）和操纵（即增强）数据集上进行了训练。在 KarNet 的四个预训练模型中，使用 DenseNet201 的模型表现出出色的诊断能力，在未增强和增强数据集上训练的模型的 AUC 分数分别为 1.00 和 0.99。