CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.
CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.
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DOI：
10.1016/j.asoc.2020.106912
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发表时间：
2021-01
影响因子：
8.7
通讯作者：
Durdu A
中科院分区：
文献类型：
--
作者：
Aslan MF;Unlersen MF;Sabanci K;Durdu A
2019冠状病毒病（COVID-2019）于2019年在中国武汉出现，并自2020年初以来在全球迅速蔓延，已感染数百万人并造成多人死亡。对于这一仍在发生的大流行病，全世界已开始动员，并采取了各种限制和预防措施，以防止这一疾病的传播。此外，必须查明感染者，以控制感染。然而，由于逆转录聚合酶链反应（RT-PCR）测试的数量不足，胸部计算机断层扫描（CT）成为辅助诊断COVID-19的流行工具。在这项研究中，提出了两种深度学习架构，可以使用胸部CT X射线图像自动检测阳性COVID-19病例。肺分割（预处理）CT图像，这是作为输入给这些建议的架构，是自动执行人工神经网络（ANN）。由于这两种架构都包含AlexNet架构，因此推荐的方法是迁移学习应用程序。然而，第二种提出的架构是一种混合结构，因为它包含双向长短期存储器（BiLSTM）层，该层还考虑了时间属性。虽然第一种架构的COVID-19分类准确率为98.14%，但在第二种混合架构中，该值为98.70%。结果证明，所提出的架构在感染检测方面表现出了出色的成功，因此，本研究在深度架构设计和高分类成功率方面都有助于以前的研究。基于人工神经网络的肺部自动分割方法。基于CNN的迁移学习-BiLSTM的新混合架构提案用于COVID-19检测。COVID-19诊断，分类准确率高。所提出的方法是简单和高性能。
Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success. ANN-based automatic segmentation method proposal for lung segmentation. A new hybrid architecture proposal based on CNN-based transfer learning–BiLSTM for COVID-19 detection. COVID-19 diagnosis with high classification accuracy. The proposed method is easy and high performance.
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影响因子：
8.3
作者：
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通讯作者：
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影响因子：
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