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首页> 外文会议>National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering >A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images
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A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images

机译：基于混合的深度转移基于Covid-19胸部X射线图像分类的方法

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摘要

As a contagious disease originating from a novel coronavirus, COVID-19 leads to swollen air sacs in the lungs. It can be diagnosed using a chest X-ray (CXR) images, which is usually cheaper and less harmful than a CT scan and is always available in small or rural hospitals. X-ray machines, however, sometimes cannot diagnose COVID-19. Since the COVID-19 dataset is small and cannot be diagnosed from CXR, pre-trained neural networks can be employed for coronavirus diagnosis. This paper mainly aims to use pre-trained deep transfer learning (DTL) architectures and conventional machine learning (ML) models as an automated instrument to diagnose COVID-19 from CXRs. To overcome the lack of a large number of images, DTL is utilized to extract image features for better classification. Then, to optimize the decision-making level for infectious diseases similar to bacterial and viral pneumonia, the extracted features are selected and classified. Our proposed method was validated by creating a new CXR database from Vasei Hospital in Sabzevar, Iran. Our hybrid model achieved hit rates above 99% and outperformed for CXR of COVID-19 and similar pneumonia classification. Comparative analysis shows the superiority of the proposed COVID-19 classification model based on DTL over other competitive methods.
机译：作为源自新型冠状病毒的传染病，Covid-19导致肺中肿胀的气囊。它可以使用胸部X射线（CXR）图像进行诊断，这通常比CT扫描更便宜，并且较少有害，并且总是在小型或农村医院中提供。然而，X射线机有时无法诊断Covid-19。由于Covid-19数据集很小并且不能从CXR诊断出来，可以使用预先训练的神经网络用于冠状病毒诊断。本文主要旨在使用预先接受训练的深度转移学习（DTL）架构和传统的机器学习（ML）模型作为自动化仪器，用于从CXRS诊断Covid-19。为了克服缺少大量图像，DTL用于提取图像特征以获得更好的分类。然后，为了优化类似于细菌和病毒性肺炎的传染病的决策水平，选择并分类提取的特征。我们提出的方法是通过在伊朗的萨比瓦尔的Vasei医院创建新的CXR数据库来验证。我们的混合模型实现了99％以上的命中率，并且对于Covid-19的CXR和类似的肺炎分类而言。比较分析显示了基于其他竞争方法的DTL所提出的Covid-19分类模型的优越性。

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