Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays
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DOI：
10.1016/j.irbm.2020.07.001
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发表时间：
2022-03-23
期刊：
影响因子：
4.8
通讯作者：
Singh, D.
中科院分区：
文献类型：
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作者：
Das, N. Narayan;Kumar, N.;Singh, D.
最广泛使用的新型冠状病毒（COVID-19）检测技术是实时聚合酶链反应（RT-PCR）。然而，RT-PCR试剂盒价格昂贵，需要6-9小时才能确认患者感染。由于RT-PCR的灵敏度较低，它提供了较高的假阴性结果。为了解决这个问题，放射成像技术，如胸部X射线和计算机断层扫描（CT）被用来检测和诊断COVID-19。在本文中，胸部X线检查优于CT扫描。这背后的原因是大多数医院都有X光机。X光机比CT扫描机便宜。除此之外，X射线的电离辐射比CT扫描低。COVID-19揭示了一些可以通过胸部X光轻松检测到的放射性特征。为此，放射科医生需要分析这些签名。然而，这是一项耗时且容易出错的任务。因此，需要自动化胸部X射线的分析。胸部X光片的自动分析可以通过基于深度学习的方法来完成，这可能会加快分析时间。这些方法可以在大数据集上训练网络的权重，也可以在小数据集上微调预训练网络的权重。然而，这些方法应用于胸部X射线非常有限。因此，本文的主要目标是开发一种基于深度迁移学习的自动化方法，通过使用极端版本的Inception（Xception）模型来检测胸部X射线中的COVID-19感染。
The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model.