COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach
COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach
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DOI：
10.1109/icmla51294.2020.00211
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
V. Sharma;Curtis E. Dyreson
中科院分区：
文献类型：
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作者：
V. Sharma;Curtis E. Dyreson
2019冠状病毒病（COVID-19）是由严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）引起的。该病毒传播迅速，其基本繁殖数（R 0）为2.2−2.7。于二零二零年三月，世界卫生组织宣布COVID-19爆发为大流行病。COVID-19目前正在影响200多个国家，有600万活跃病例。有效的COVID-19检测策略对于控制疫情至关重要，但对检测的需求超过了使用逆转录聚合酶链反应（RT-PCR）的检测试剂盒的可用性。在本文中，我们提出了一种使用人工智能筛查COVID-19的技术。我们的技术只需要几秒钟就能检测出病人体内是否有病毒。我们收集了一个胸部X射线图像数据集，并训练了几个流行的基于深度卷积神经网络的模型（VGG，MobileNet，Xception，DenseNet，InceptionResNet）来对胸部X射线进行分类。对这些模型不满意，我们随后设计并构建了一个剩余注意力网络，该网络能够以98%的测试准确率和100%的验证准确率筛选COVID-19。我们模型的特征图视觉显示胸部X射线中对分类很重要的区域。我们的工作可以帮助提高AI辅助应用在临床实践中的适应性。该项目中使用的代码和数据集可在https://github.com/vishalshar/covid-19-screening-using-RAN-on-X-ray-images上获得。
Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number (R0) of 2.2−2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to screen for COVID-19 using artificial intelligence. Our technique takes only seconds to screen for the presence of the virus in a patient. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify the chest X-rays. Unsatisfied with these models, we then designed and built a Residual Attention Network that was able to screen COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A feature maps visual of our model show areas in a chest X-ray which are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice. The code and dataset used in this project are available at https://github.com/vishalshar/covid-19-screening-using-RAN-on-X-ray-images.