论文标题
使用深神经网络和LightGBM从胸部X射线图像中诊断COVID-19病例
Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM
论文作者
论文摘要
2019年底在中国武汉发现了冠状病毒，然后在全球爆发迅速的爆发中导致了大流行。从那时起，受感染者的数量一直在迅速增加。因此，在这项研究中，尝试使用深层神经网络（DNN）提出了一种新的有效方法来自动从X射线图像中自动诊断电晕疾病。在提出的方法中，使用DENSNET169来提取患者胸部X射线（CXR）图像的特征。将提取的特征赋予特征选择算法（即ANOVA），以选择其中许多。最后，通过LightGBM算法对所选特征进行了分类。在ChestX-Ray8数据集上评估了所提出的方法，并在两级（即Covid-19和no-Findings）和多级级别（即COVID-19，covid-19，肺炎，肺炎和无调查）分类问题中达到99.20％和94.22％的精度。
The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively.
