Since December 2019, the novel COVID-19’s spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations. In the literature, AI-driven tools are limited to one data type either CT scan or CXR to detect COVID-19 positive cases. Integrating multiple data types could possibly provide more information in detecting anomaly patterns due to COVID-19. Therefore, in this paper, we engineered a Convolutional Neural Network (CNN) -tailored Deep Neural Network (DNN) that can collectively train/test both CT scans and CXRs. In our experiments, we achieved an overall accuracy of 96.28% (AUC = 0.9808 and false negative rate = 0.0208). Further, major existing DNNs provided coherent results while integrating CT scans and CXRs to detect COVID-19 positive cases.

自 2019 年 12 月以来，新型 COVID-19 的传播率呈指数级增长，并使用 AI 驱动的工具来防止进一步传播 [1]。它们可以帮助预测、筛查和诊断 COVID-19 阳性病例。在此范围内，计算机断层扫描 (CT) 扫描和胸部 X 射线 (CXR) 成像广泛用于大规模分类情况。在文献中，人工智能驱动的工具仅限于一种数据类型，即 CT 扫描或 CXR 来检测 COVID-19 阳性病例。集成多种数据类型可能会在检测由 COVID-19 引起的异常模式方面提供更多信息。因此，在本文中，我们设计了一个卷积神经网络 (CNN) 定制的深度神经网络 (DNN)，可以共同训练/测试 CT 扫描和 CXR。在我们的实验中，我们实现了 96.28% 的总体准确率（AUC = 0.9808，假阴性率 = 0.0208）。