In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate. The open-source community collectively has made efforts to collect and annotate the data, but it is not enough to train an accurate deep learning model. Few-shot learning is a sub-field of machine learning that aims to learn the objective with less amount of data. In this work, we have experimented with well-known solutions for data scarcity in deep learning to detect COVID-19. These include data augmentation, transfer learning, and few-shot learning, and unsupervised learning. We have also proposed a custom few-shot learning approach to detect COVID-19 using siamese networks. Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data. Using our proposed approach we were able to achieve 96.4% accuracy an improvement from 83% using baseline models.

翻译：在目前COVID-19大流行的情况下,迫切需要迅速准确地筛查感染的病人。使用在胸X光图像方面受过训练的深学习模型,可以成为在这种情况下筛查COVID-19病人的有效方法。深层次的学习方法已经在医疗界广泛使用。但是,它们需要大量的数据才能准确。开放源社区集体努力收集和注解数据,但不足以培养准确的深层次学习模式。少见的学习是一个机器学习的子领域,目的是用较少的数据来了解目标。在这项工作中,我们实验了众所周知的在深层学习中数据稀缺的解决方案,以探测COVID-19,其中包括数据增强、传输学习和少见的学习,以及未经监督的学习。我们还提议了一种定制的少见的学习方法,以利用Siameese网络探测COVID-19。我们的实验结果显示,我们可以通过采用少量的数据学习方法来实施高效和准确的深层次的COVID-19探测模型。我们用不到多少的数据模型来改进了数据。我们利用了83 %的基准方法,我们用了86 %的精确度实现了。