The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

中文翻译：

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FUSI-CAD：基于 CNN 和手工特征融合的冠状病毒 (COVID-19) 诊断

在最初阶段对冠状病毒（COVID-19）进行精确、快速的诊断有助于医生在高工作量的情况下管理患者。此外，它还可以防止这种大流行病毒的传播。基于人工智能（AI）技术的计算机辅助诊断（CAD）可用于从计算机断层扫描（CT）成像中区分COVID-19和非COVID-19。此外，与实验室测试相比，CAD 系统能够提供准确、快速的 COVID-19 诊断，从而节省疾病控制时间并提供有效的诊断。在这项研究中，提出了一种基于人工智能技术的新型 CAD 系统，称为 FUSI-CAD。文献中几乎所有的方法都是基于单独的卷积神经网络（CNN）。因此，FUSI-CAD 系统基于多种不同 CNN 架构与三种手工特征的融合，包括统计特征和纹理分析特征，例如离散小波变换 (DWT) 和灰度共生矩阵 (GLCM)，以前未用于冠状病毒诊断。SARS-CoV-2 CT 扫描数据集用于测试所提出的 FUSI-CAD 的性能。结果表明，所提出的系统可以准确地区分 COVID-19 和非 COVID-19 图像，准确率达到 99%。此外，该系统也被证明是可靠的。这是因为灵敏度、特异性和精确度达到了 99%。此外，诊断优势比（DOR）≥100。此外，将结果与基于相同数据集的近期相关研究进行了比较。比较验证了所提出的 FUSI-CAD 相对于其他相关 CAD 系统的能力。因此，新颖的FUSI-CAD系统可以应用于真实的诊断场景，实现对COVID-19的准确检测，并避免由于人体疲劳而可能存在的误诊。它还可以减少放射科医生在检查过程中所花费的时间和精力。