Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works.

在放射成像数据中，胸部 X 射线 (CXR) 在观察 COVID-19 表现方面非常有用。对于使用 CXR 的大规模筛查，必须使用计算效率高的 AI 驱动工具从非 COVID 病例中检测 COVID-19 阳性病例。为此，我们提出了一种轻量级卷积神经网络 (CNN) 定制的浅层架构，该架构可以使用 CXR 自动检测 COVID-19 阳性病例，而不会出现假阴性。与其他深度学习模型相比，浅层 CNN 定制架构的设计参数更少。使用 321 个 COVID-19 阳性 CXR 验证了浅层 CNN 定制架构。除了 COVID-19 阳性病例外，还考虑了另一组非 COVID-19 5856 病例（可公开获得，来源：Kaggle），包括正常、病毒和细菌性肺炎病例。在我们的实验测试中，为了避免可能的偏差，遵循 5 折交叉验证，并使用平衡和不平衡的数据集。所提出的模型达到了 99.69% 的最高准确率，灵敏度为 1.0，其中 AUC 为 0.9995。此外，对于 5856 例 COVID-19 阴性病例，报告的假阳性率仅为 0.0015。我们的结果表明，所提出的 CNN 可能用于大规模筛选。使用完全相同的一组 CXR 集合，当前的结果优于其他深度学习模型和主要的最先进的作品。对于 5856 例 COVID-19 阴性病例，报告的假阳性率仅为 0.0015。我们的结果表明，所提出的 CNN 可能用于大规模筛选。使用完全相同的一组 CXR 集合，当前的结果优于其他深度学习模型和主要的最先进的作品。对于 5856 例 COVID-19 阴性病例，报告的假阳性率仅为 0.0015。我们的结果表明，所提出的 CNN 可能用于大规模筛选。使用完全相同的一组 CXR 集合，当前的结果优于其他深度学习模型和主要的最先进的作品。