Elazab Ahmed, Elfattah Mohamed Abd, Zhang Yuexin
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.
Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt.
Appl Soft Comput. 2022 Jan;114:108041. doi: 10.1016/j.asoc.2021.108041. Epub 2021 Nov 16.
The novel Coronavirus disease 2019 (COVID-2019) has become a global pandemic and affected almost all aspects of our daily life. The total number of positive COVID-2019 cases has exponentially increased in the last few months due to the easy transmissibility of the virus. It can be detected using the nucleic acid test or the antibodies blood test which are not always available and take several hours to get the results. Therefore, researchers proposed computer-aided diagnosis systems using the state-of-the-art artificial intelligence techniques to learn imaging biomarkers from chest computed tomography and X-ray radiographs to effectively diagnose COVID-19. However, previous methods either adopted transfer learning from a pre-trained model on natural images or were trained on limited datasets. Either cases may lead to accuracy deficiency or overfitting. In addition, feature space suffers from noise and outliers when collecting X-ray images from multiple datasets. In this paper, we overcome the previous limitations by firstly collecting a large-scale X-ray dataset from multiple resources. Our dataset includes 11,312 images collected from 10 different data repositories. To alleviate the effect of the noise, we suppress it in the feature space of our new dataset. Secondly, we introduce a supervision mechanism and combine it with the VGG-16 network to consider the differences between the COVID-19 and healthy cases in the feature space. Thirdly, we propose a multi-site (center) COVID-19 graph convolutional network (GCN) that exploits dataset information, the status of training samples, and initial scores to effectively classify the disease status. Extensive experiments using different convolutional neural network-based methods with and without the supervision mechanism and different classifiers are performed. Results demonstrate the effectiveness of the proposed supervision mechanism in all models and superior performance with the proposed GCN.
2019年新型冠状病毒病（COVID-2019）已成为全球大流行疾病，几乎影响到我们日常生活的方方面面。在过去几个月里，由于该病毒易于传播，COVID-2019阳性病例总数呈指数级增长。可以使用核酸检测或抗体血液检测来检测该病毒，但这些检测并非总能进行，而且需要数小时才能得到结果。因此，研究人员提出了使用最先进的人工智能技术的计算机辅助诊断系统，以从胸部计算机断层扫描和X光片中学习成像生物标志物，从而有效诊断COVID-19。然而，以前的方法要么采用从预训练的自然图像模型进行迁移学习，要么在有限的数据集上进行训练。这两种情况都可能导致准确性不足或过度拟合。此外，从多个数据集中收集X光图像时，特征空间会受到噪声和离群值的影响。在本文中，我们首先从多个来源收集大规模X光数据集，克服了以前的局限性。我们的数据集包括从10个不同数据存储库收集的11312张图像。为了减轻噪声的影响，我们在新数据集的特征空间中对其进行抑制。其次，我们引入一种监督机制，并将其与VGG-16网络相结合，以考虑COVID-19病例和健康病例在特征空间中的差异。第三，我们提出了一种多站点（中心）COVID-19图卷积网络（GCN），该网络利用数据集信息、训练样本状态和初始分数来有效分类疾病状态。我们使用基于不同卷积神经网络的方法，在有无监督机制以及不同分类器的情况下进行了广泛的实验。结果证明了所提出的监督机制在所有模型中的有效性以及所提出的GCN的卓越性能。
