Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images
Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images
复制标题
使用基于胸部放射图像的深度学习开发用于早期检测 COVID-19 的临床决策支持系统
DOI：
10.1109/iscv49265.2020.9204282
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
H. Qjidaa
中科院分区：
文献类型：
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作者：
M. Qjidaa;A. Ben;Y. Mechbal;H. Amakdouf;M. Maaroufi;B. Alami;H. Qjidaa
为了控制COVID-19病毒的传播，并在控制疾病传播方面赢得关键时间，迫切需要基于人工智能的快速准确的诊断方法。在这篇文章中，我们提出了一个基于胸部X光图像的深度学习的COVID 19早期检测临床决策支持系统。为此，我们将开发一种深度学习方法，可以提取COVID-19的图形特征，以便在病原体测试之前提供临床诊断。为此，我们收集了100张经病原体确诊的COVID-19病例的图像，100张诊断为典型病毒性肺炎的图像和100张正常病例的图像。所提出的模型的架构首先通过输入图像的预处理，然后增加数据。然后，模型开始提取特征的步骤，然后是学习步骤。最后，该模型使用由多个分类器形成的完全连接的网络开始分类和预测过程。使用VGG卷积神经网络进行深度学习和分类。所提出的模型实现了92.5%的内部验证和87.5%的外部验证的准确性。对于AUC标准，我们在内部验证和外部验证中分别获得了97%和95%的值。关于灵敏度标准，我们在内部验证中获得了92%的值，在外部验证中获得了87%的值。我们的模型在测试阶段获得的结果表明，我们的模型在检测COVID-19方面非常有效，可以作为COVID-19检测中精确，快速和有效的临床决策支持系统提供给卫生社区。
To control the spread of the COVID-19 virus and to gain critical time in controlling the spread of the disease, rapid and accurate diagnostic methods based on artificial intelligence are urgently needed. In this article, we propose a clinical decision support system for the early detection of COVID 19 using deep learning based on chest radiographic images. For this we will develop an in-depth learning method which could extract the graphical characteristics of COVID-19 in order to provide a clinical diagnosis before the test of the pathogen. For this, we collected 100 images of cases of COVID-19 confirmed by pathogens, 100 images diagnosed with typical viral pneumonia and 100 images of normal cases. The architecture of the proposed model first goes through a preprocessing of the input images followed by an increase in data. Then the model begins a step to extract the characteristics followed by the learning step. Finally, the model begins a classification and prediction process with a fully connected network formed of several classifiers. Deep learning and classification were carried out using the VGG convolutional neural network. The proposed model achieved an accuracy of 92.5% in internal validation and 87.5% in external validation. For the AUC criterion we obtained a value of 97% in internal validation and 95% in external validation. Regarding the sensitivity criterion, we obtained a value of 92% in internal validation and 87% in external validation. The results obtained by our model in the test phase show that our model is very effective in detecting COVID-19 and can be offered to health communities as a precise, rapid and effective clinical decision support system in COVID-19 detection.