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An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis

机译：一种不确定性意识的Covid-19诊断的转移学习框架

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摘要

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.
机译：早期可靠地检测Covid-19受感染的患者对于预防和限制其爆发至关重要。 Covid-19检测的PCR测试在许多国家不可用，并且还有真正担心其可靠性和性能。这些缺点的动机，本文提出了使用医学图像的Covid-19检测的深度不确定性感知转移学习框架。首先应用包括VGG16，Reset50，DenSenet121和InceptionResNetv2的四个流行的卷积神经网络（CNN），包括从胸部X射线和计算机断层扫描（CT）图像中提取深度特征。然后通过不同的机器学习和统计建模技术处理提取的特征以识别Covid-19案例。我们还计算并报告分类结果的认知不确定性，以确定培训模型对其决定不信任的地区（出于分销问题）。 X射线和CT图像数据集的综合仿真结果表明，线性支持向量机和神经网络模型实现了通过接收器操作特性（ROC）曲线（AUC）下的精度，灵敏度，特异性和面积测量的最佳结果。此外，发现CT图像与X射线图像相比，CT图像的预测不确定性估计要高得多。

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