论文标题
基于深视觉单词的新型功能，以分类胸部X射线图像，用于19诊断
New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
论文作者
论文摘要
由于严重急性呼吸道综合征冠状病毒2（COVID-19）的感染会导致肺部类似肺炎的作用，因此胸部X射线检查可以帮助诊断疾病。为了自动分析图像，它们由一组语义特征在机器中表示。深度学习（DL）模型被广泛用于从图像中提取特征。一般的深度特征可能不适合表示胸部X射线，因为它们具有几个语义区域。尽管基于视觉单词（BOVW）的特征显示更适合X射线图像类型的图像，但现有的BOVW特征可能无法捕获足够的信息来区分Covid-19的感染与其他与肺炎相关的感染。在本文中，我们通过删除特征映射标准化步骤并在原始功能映射上添加了深层特征步骤，提出了一种新的BOVW方法（称为深视觉单词袋（BODVW）），提出了一种新的BOVW方法。这有助于保留每个特征图的语义，这些特征图可能具有重要的线索，可以将Covid-19与肺炎区分开。我们使用支持向量机（SVM）评估了我们提出的BODVW特征在胸部X射线分类中的有效性，以诊断COVID-19。我们对公开可用的COVID-19 X射线数据集的结果表明，与最先进的方法相比，在较短的计算时间内，在较短的计算时间内，我们的功能产生了稳定而突出的分类精度，尤其是将Covid-19与其他肺炎区分开来。因此，我们的方法可能是一个非常有用的工具，可以大规模快速诊断Covid-19患者。
Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the pneumonia-like effect in the lungs, the examination of chest x-rays can help to diagnose the diseases. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features may not be appropriate to represent chest x-rays as they have a few semantic regions. Though the Bag of Visual Words (BoVW) based features are shown to be more appropriate for x-ray type of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other pneumonia-related infections. In this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from pneumonia. We evaluate the effectiveness of our proposed BoDVW features in chest x-rays classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on a publicly available COVID-19 x-ray dataset reveal that our features produce stable and prominent classification accuracy, particularly differentiating COVID-19 infection from other pneumonia, in shorter computation time compared to the state-of-the-art methods. Thus, our method could be a very useful tool for quick diagnosis of COVID-19 patients on a large scale.
