Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

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针对不平衡 COVID-19 数据集使用深度学习的新型感知两层图像融合

背景和目的 COVID-19 是一种新型病毒，可​​导致全球范围内的生命停止。此时，新型冠状病毒COVID-19正在全球范围内迅速传播，对人们的健康构成威胁。实验医学测试和分析表明，几乎所有 COVID-19 患者都会发生肺部感染。虽然胸部计算机断层扫描是诊断与肺部相关疾病的有用成像方法，但胸部 X 射线 (CXR) 更广泛可用，主要是由于其价格和结果较低。深度学习 (DL) 是一种重要的流行人工智能技术，是帮助医生分析大量 CXR 图像对性能至关重要的有效方法。材料和方法在这篇文章中，我们提出了一种新颖的感知两层图像融合，使用 DL 为 COVID-19 数据集获取更多信息的 CXR 图像。为了评估所提出的算法性能，用于这项工作的数据集包括从 25 个病例中获取的 87 个 CXR 图像，所有这些图像都通过 COVID-19 得到确认。需要数据集预处理来促进卷积神经网络 (CNN) 的作用。因此，使用非下采样 Contourlet 变换 (NSCT) 和 CNN_VGG19 作为特征提取器的混合分解和融合。结果我们的实验结果表明，此处建立的算法可以可靠地生成不平衡的 COVID-19 数据集。与使用的 COVID-19 数据集相比，融合图像具有更多的特征和特征。在评估性能测量中，应用了六个指标，例如 QAB/F、QMI、PSNR、SSIM、SF 和 STD，确定各种医学图像融合（MIF）的评价。在QMI、PSNR、SSIM中，提出的算法NSCT + CNN_VGG19实现最大，在融合图像中发现的特征特征最大。我们可以推断，所提出的融合算法足以有效地生成 CXR COVID-19 图像，这些图像对于检查者探索患者状态更有用。结论对于不平衡的 COVID-19 数据集使用 DL 的新型图像融合算法是这项工作的关键贡献。大量实验结果表明，所提出的算法 NSCT + CNN_VGG19 优于竞争性图像融合算法。所提出的算法 NSCT + CNN_VGG19 达到了最大，在融合图像中发现的特征特征最大。我们可以推断，所提出的融合算法足以有效地生成 CXR COVID-19 图像，这些图像对于检查者探索患者状态更有用。结论对于不平衡的 COVID-19 数据集使用 DL 的新型图像融合算法是这项工作的关键贡献。大量实验结果表明，所提出的算法 NSCT + CNN_VGG19 优于竞争性图像融合算法。所提出的算法 NSCT + CNN_VGG19 达到了最大，在融合图像中发现的特征特征最大。我们可以推断，所提出的融合算法足以有效地生成 CXR COVID-19 图像，这些图像对于检查者探索患者状态更有用。结论对于不平衡的 COVID-19 数据集使用 DL 的新型图像融合算法是这项工作的关键贡献。大量实验结果表明，所提出的算法 NSCT + CNN_VGG19 优于竞争性图像融合算法。结论对于不平衡的 COVID-19 数据集使用 DL 的新型图像融合算法是这项工作的关键贡献。大量实验结果表明，所提出的算法 NSCT + CNN_VGG19 优于竞争性图像融合算法。结论对于不平衡的 COVID-19 数据集使用 DL 的新型图像融合算法是这项工作的关键贡献。大量实验结果表明，所提出的算法 NSCT + CNN_VGG19 优于竞争性图像融合算法。