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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多层深度聚合增强网络，可从大规模异类射线照相数据中识别COVID-19感染。

在目前的2019年冠状病毒疾病流行病（COVID-19）中，诸如X射线和计算机断层扫描（CT）等放射成像模式已被确认为有效的诊断工具。但是，放射线检查的主观评估是一项耗时的工作，需要放射线专家。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，并协助医学专家做出有效的诊断决策。在这项工作中，我们提出了一种最佳的多层深度聚集增强网络，以从包括X射线和CT图像在内的各种射线照相数据中识别出COVID-19感染。我们的方法通过互惠互利的方法，利用多层次的深度聚合功能和多阶段训练来最大化总体CAD性能。为了改善对CAD预测的解释，这些多级深度特征被可视化为可帮助放射科医生验证CAD结果的附加输出。总共将六个公开可用的数据集融合在一起，以构建单个大规模异类射线照相集合，该集合用于分析所提出的技术和其他基准方法的性能。为了保持方法的通用性，我们选择了不同的患者数据进行培训，验证和测试，因此，同一患者的数据未包括在培训，验证和测试子集中。此外，在所有实验中进行了五重交叉验证，以进行公正的评估。我们的方法在平均准确度，F度量，