论文标题
用于预测计算机断层扫描和X射线图像的冠状病毒疾病的深度放射分析
Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images
论文作者
论文摘要
本文建议使用高斯混合模型编码从卷积神经网络中学到的特征的分布。这些参数特征称为GMM-CNN，源自胸部计算机断层扫描和2019年冠状病毒病患者的X射线扫描。我们使用所提出的GMM-CNN特征作为基于随机森林的强大分类器的输入，以区别于covid-19和其他肺炎病例。与测试图像上的标准CNN分类相比，我们的实验评估了GMM-CNN特征的优势。使用随机森林分类器（用于训练的80 \％样品； 20 \％样品进行测试），用两个混合组件编码的GMM-CNN功能比标准CNN分类（P \，$ <$ \，0.05）提供了明显更好的性能。具体而言，我们的方法达到了96.00 \， - \，96.70 \％的准确性，而ROC曲线下的面积在99.29 \， - \，99.45 \％的范围内，并通过将计算机Tomagraphy和X-Ray图像中的GMM-CNN功能相结合而获得的最佳性能。我们的结果表明，提出的GMM-CNN特征可以改善胸部计算机断层扫描和X射线扫描中Covid-19的预测。
This paper proposes to encode the distribution of features learned from a convolutional neural network using a Gaussian Mixture Model. These parametric features, called GMM-CNN, are derived from chest computed tomography and X-ray scans of patients with Coronavirus Disease 2019. We use the proposed GMM-CNN features as input to a robust classifier based on random forests to differentiate between COVID-19 and other pneumonia cases. Our experiments assess the advantage of GMM-CNN features compared to standard CNN classification on test images. Using a random forest classifier (80\% samples for training; 20\% samples for testing), GMM-CNN features encoded with two mixture components provided a significantly better performance than standard CNN classification (p\,$<$\,0.05). Specifically, our method achieved an accuracy in the range of 96.00\,--\,96.70\% and an area under the ROC curve in the range of 99.29\,--\,99.45\%, with the best performance obtained by combining GMM-CNN features from both computed tomography and X-ray images. Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest computed tomography and X-ray scans.
