论文标题
从X射线图像中诊断出COVID-19疾病的堆叠卷积神经网络
Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images
论文作者
论文摘要
在2020年SARS-COV-2的这种大流行状况中，从胸部X射线图像中对Covid-19的自动和快速筛选已成为迫切需要的。但是，由于Covid-19和X射线图像中Covid-19和其他病毒性肺炎之间的差异，对患者的准确且可靠的筛查是巨大的挑战。在本文中，我们设计了一种新的堆叠卷积神经网络模型，用于自动诊断胸部X射线图像从胸部X射线图像中自动诊断。我们从VGG19获得不同的子模型，并在训练期间开发了30层的CNN模型（称为Covnet30），并使用Logistic回归将获得的子模型一起堆叠在一起。提出的CNN模型结合了不同CNN的子模型的区分功能，并将胸部X射线图像分为Covid-19，正常和肺炎类。此外，我们生成的X射线图像数据集称为covid19cxr，其中包括来自三个公开数据存储库中1768名患者的2764张胸部X射线图像。提出的堆叠CNN的精度为92.74％，灵敏度为93.33％，PPV为92.13％，F1分数为X射线图像的分类为0.93。我们提出的方法表明，它优于X射线图像中Covid-19的现有方法的优越性。
Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images.
