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COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

机译：Covid-Caps：一种基于胶囊网络的框架，用于识别X射线图像的Covid-19案例

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摘要

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%. (C) 2020 Elsevier B.V. All rights reserved.
机译：新型冠状病毒病（Covid-19）在21世纪二十多年结束时突然改变了世界。 Covid-19非常具有传染性，并迅速传播全球展开，其早期诊断至关重要。 Covid-19的早期诊断使医疗保健专业人员和政府当局打破过渡链并压平流行曲线。然而，Covid-19诊断测试的常见类型需要特定的设备并且具有相对较低的灵敏度。另一方面，计算机断层扫描（CT）扫描和X射线图像揭示了与这种疾病相关的特定表现。与其他肺部感染重叠使人以人为本的Covid-19挑战性诊断。因此，迫切感兴趣的是开发深神经网络（DNN）的诊断解决方案，主要基于卷积神经网络（CNNS），以促进鉴定阳性Covid-19案例。但是，CNNS易于在图像实例之间丢失空间信息，并且需要大型数据集。本文提出了一种基于胶囊网络的替代建模框架，称为Covid-Caps，能够处理小型数据集，这是由于Covid-19的突然和快速出现而具有重要意义。我们的结果基于X射线图像数据集显示Covid-Caps在以前的基于CNN的模型中有优势。 Covid-Caps的精度为95.7％，灵敏度为90％，特异性为95.8％，曲线下的面积（AUC）为0.97，同时与其对应物相比具有较少的培训参数。为了潜在而进一步提高Covid-Caps的诊断能力，基于由X射线图像的外部数据集构成的新数据集使用预训练和转移学习。这与现有的Covid-19检测作品相反，基于自然图像进行预训练。使用类似性的数据集进行预培训，进一步提高了98.3％和特异性的准确度至98.6％。 （c）2020 Elsevier B.v.保留所有权利。

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