利用深度学习方法从x射线图像中检测Covid-19

G. Sapountzakis, P. Theofilou, P. Tzouveli
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引用次数: 0

摘要

COVID-19是一种严重的高度传染性疾病,可引起呼吸系统感染。其严重程度因人而异。很多时候,病人的健康状况迅速恶化,他们需要专门的医疗护理。因此,早期可靠的检测非常重要。胸部x线(CXR)成像作为一种简单、方便、经济、有效的新冠肺炎诊断手段被广泛应用。在这项工作中,我们使用此类图像的最大数据集covid - q - ex数据集,旨在构建一个深度学习系统,以在COVID-19疾病的检测问题中产生高效可靠的预测。因此,我们专注于研究和比较最先进的深度卷积神经网络(DCNNs)用于CXR图像分类,并使用Grad-CAM来测试其可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covid-19 Detection From X-Rays Images Using Deep Learning Methods
COVID-19 is a serious and highly contagious disease that causes infection of the respiratory system. Its severity varies from person to person. Many times patients’ health deteriorates rapidly and they need specialized medical care. For this reason, its early and reliable detection is important. Chest X-ray (CXR) imaging is widely used as an easy, accessible, economical and valid mean of COVID-19 diagnosis. In this work, using the COVID-QU-Ex dataset, which is the largest dataset of such images, we aim to build a deep learning system to produce efficient and reliable predictions in the detection problem of the disease COVID-19. Therefore, we focus on the study and comparison of state-of-the-art Deep Convolutional Neural Networks (DCNNs) for classification of CXR images and we use Grad-CAM to test their explainability.
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