Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.
Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.
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DOI：
10.1016/j.eswa.2021.115681
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发表时间：
2021-12-15
影响因子：
8.5
通讯作者：
Hortas MO
中科院分区：
文献类型：
--
作者：
Morís DI;de Moura Ramos JJ;Buján JN;Hortas MO
当前的COVID-19大流行已在全球造成超过1亿例病例和200多万例死亡，尽管缺乏可用样本，但仍需要开发快速准确的诊断方法。这种疾病主要影响患者的呼吸系统，并可导致肺炎和急性呼吸综合征的严重病例，导致肺部形成几种病理结构。这些病理结构可以利用胸部X线成像来探索。作为对保健服务的一项建议，应使用便携式胸部X光设备，而不是传统的固定机器，以防止病原体的传播。然而，便携式设备存在几个问题（特别是与捕获质量相关的问题）。此外，临床医生的主观性和疲劳导致诊断过程非常困难。为了克服这一点，计算机辅助方法可能非常有用，即使考虑到缺乏COVID-19影响所显示的可用样本。在这项工作中，我们提出了一种改进COVID-19筛查性能的方法，利用几个循环生成对抗网络来生成有用和相关的合成图像，以解决从便携式设备获得的低质量和低细节数据集的背景下COVID-19样本的缺乏问题。为了验证这种改进COVID-19筛查的建议，进行了几个实验。结果表明，这种数据增强策略提高了先前COVID-19筛查方案的性能，在区分非COVID-19（即正常对照样本和具有COVID-19以外病理的样本）和真正COVID-19样本时，准确率达到98. 61%。值得注意的是，这种方法可以外推到其他肺部病变，甚至其他医学成像领域，以克服数据稀缺。
The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 (i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.
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