Background: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. Objective: Machine vision–based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)–based algorithm. Methods: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. Results: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. Conclusions: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non–COVID-19 ones without any error in the application phase. Overall, the proposed deep learning–based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.

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中文翻译：

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基于深度卷积神经网络的使用多次肺部扫描的 COVID-19 计算机辅助检测系统：设计和实现研究

背景：由于 COVID-19 大流行以及财政、医院和医疗资源耗尽后医疗保健系统即将崩溃，世界卫生组织将 COVID-19 大流行的警报级别从高调整为非常高。与此同时，更具成本效益和更精确的 COVID-19 检测方法正在全球范围内受到青睐。目的：基于机器视觉的 COVID-19 检测方法，特别是深度学习作为大流行早期阶段的诊断方法，在大流行期间受到了高度重视。本研究旨在通过使用基于神经搜索架构网络 (NASNet) 的算法，设计针对 COVID-19 的高效计算机辅助检测 (CAD) 系统。方法：NASNet是一种用于图像特征提取的最先进的预训练卷积神经网络，用于识别疾病早期的COVID-19患者。使用了本地数据集，其中包括 190 名患有 COVID-19 的患者和 59 名未患有 COVID-19 的患者的 10,153 次计算机断层扫描。结果：在对训练数据集进行拟合、超参数调整和分类器块的拓扑更改之后，所提出的基于 NASNet 的模型在测试数据集上进行了评估，并取得了显着的结果。该模型的检测灵敏度、特异性和准确度分别为 0.999、0.986 和 0.996。结论：所提出的模型在 2 个数据类的分类中取得了可接受的结果。因此，基于该模型设计了一个 CAD 系统，用于使用多次肺部计算机断层扫描检测 COVID-19。该系统将所有 COVID-19 病例与非 COVID-19 病例区分开来，在申请阶段没有出现任何错误。总体而言，所提出的基于深度学习的 CAD 系统可以极大地帮助放射科医生在早期阶段检测到 COVID-19。在 COVID-19 大流行期间，使用 CAD 系统作为筛查工具将加速疾病检测并防止医疗保健资源的损失。

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