论文标题
不确定性感知转移学习的基于COVID-19诊断的框架
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
论文作者
论文摘要
早期且可靠的检测是19被感染的患者，对于预防和限制其爆发至关重要。在许多国家 /地区无法获得COVID-19检测的PCR测试，并且对其可靠性和性能也有真正的担忧。在这些缺点的推动下，本文提出了使用医学图像进行COVID-19检测的深层不确定性感知转移学习框架。首先应用了四个流行的卷积神经网络（CNN），包括VGG16，RESNET50，DENSENET121和INCEPTIONRESNETV2。然后，通过不同的机器学习和统计建模技术来处理提取的特征，以识别Covid-19情况。我们还计算并报告了分类结果的认知不确定性，以确定训练有素的模型对其决策不信心的区域（出于分配问题）。 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获得了通过准确性，灵敏度，特异性和AUC衡量的最佳结果。还发现，与X射线图像相比，CT图像的预测不确定性估计值要高得多。
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 paper 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 modelling 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 datasets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and AUC. Also it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.
