In the present epidemic of the coronavirus disease 2019 (COVID-19), radiological imaging modalities, such as X-ray and computed tomography (CT), have been identified as effective diagnostic tools. However, the subjective assessment of radiographic examination is a time-consuming task and demands expert radiologists. Recent advancements in artificial intelligence have enhanced the diagnostic power of computer-aided diagnosis (CAD) tools and assisted medical specialists in making efficient diagnostic decisions. In this work, we propose an optimal multilevel deep-aggregated boosted network to recognize COVID-19 infection from heterogeneous radiographic data, including X-ray and CT images. Our method leverages multilevel deep-aggregated features and multistage training via a mutually beneficial approach to maximize the overall CAD performance. To improve the interpretation of CAD predictions, these multilevel deep features are visualized as additional outputs that can assist radiologists in validating the CAD results. A total of six publicly available datasets were fused to build a single large-scale heterogeneous radiographic collection that was used to analyze the performance of the proposed technique and other baseline methods. To preserve generality of our method, we selected different patient data for training, validation, and testing, and consequently, the data of same patient were not included in training, validation, and testing subsets. In addition, fivefold cross-validation was performed in all the experiments for a fair evaluation. Our method exhibits promising performance values of 95.38%, 95.57%, 92.53%, 98.14%, 93.16%, and 98.55% in terms of average accuracy, F-measure, specificity, sensitivity, precision, and area under the curve, respectively and outperforms various state-of-the-art methods.

中文翻译：

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从大规模异构放射数据中识别 COVID-19 感染的多级深度聚合增强网络

在当前流行的 2019 年冠状病毒病 (COVID-19) 中，X 射线和计算机断层扫描 (CT) 等放射成像方式已被确定为有效的诊断工具。然而，放射检查的主观评估是一项耗时的任务，需要放射科专家。人工智能的最新进展增强了计算机辅助诊断 (CAD) 工具的诊断能力，并帮助医学专家做出有效的诊断决策。在这项工作中，我们提出了一个最佳的多级深度聚合增强网络，以从包括 X 射线和 CT 图像在内的异构放射学数据中识别 COVID-19 感染。我们的方法通过互惠互利的方法利用多级深度聚合特征和多阶段训练，以最大限度地提高整体 CAD 性能。为了改进 CAD 预测的解释，这些多级深度特征被可视化为可以帮助放射科医生验证 CAD 结果的附加输出。共融合了六个公开可用的数据集，以构建一个单一的大型异质放射照相集合，用于分析所提出的技术和其他基线方法的性能。为了保持我们方法的通用性，我们选择了不同的患者数据进行训练、验证和测试，因此，同一患者的数据不包括在训练、验证和测试子集中。此外，为了公平评估，在所有实验中进行了五重交叉验证。我们的方法在平均准确度、F-measure、