论文标题
Ensemble-CVDNET：使用网络合奏的COVID-19检测基于深度学习的端到端分类框架
Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks
论文作者
论文摘要
2019年12月在中国武汉开始的新型冠状病毒疾病（Covid-19）继续迅速蔓延，影响了整个世界。具有高度敏感的诊断筛查工具以尽早检测疾病是至关重要的。目前，胸部CT成像是通过放射学成像评估Covid-19肺炎的主要筛选工具。但是，CT成像需要更大的辐射剂量，更长的暴露时间，更高的成本，并且可能会受到患者运动的影响。 X射线成像是一种快速，便宜，更适合患者友好的，并且几乎在每个医疗机构中都可以使用。因此，我们专注于X射线图像，并开发了一种端到端的深度学习模型，即Ensemble-CVDNET，以区分Covid-19肺炎与非卵巢肺炎和健康病例中的肺炎和健康病例。所提出的模型基于三个轻巧的预训练模型的组合，在不同深度下，Shufflenet和EfficityNet-B0，并在不同的抽象水平上结合了特征地图。在提出的端到端模型中，微调后将网络并行用作特征提取器，并且在其顶部使用了一些其他层。提出的模型在COVID-19射线照相数据库中进行了评估，该数据库由219 Covid-19、1341 Healthy和1345病毒性肺炎胸部X射线X射线图像组成。实验结果表明，我们的轻量级集合-CVDNET模型提供了98.30％的精度，97.78％的灵敏度和97.61％的F1得分，仅使用562万参数。此外，使用建议的方法使用中级GPU进行处理并预测X射线图像大约需要10毫秒。我们认为，这项研究中提出的方法可能是放射科医生在早期诊断疾病中有用的诊断筛查工具。
The new type of coronavirus disease (COVID-19), which started in Wuhan, China in December 2019, continues to spread rapidly affecting the whole world. It is essential to have a highly sensitive diagnostic screening tool to detect the disease as early as possible. Currently, chest CT imaging is preferred as the primary screening tool for evaluating the COVID-19 pneumonia by radiological imaging. However, CT imaging requires larger radiation doses, longer exposure time, higher cost, and may suffer from patient movements. X-Ray imaging is a fast, cheap, more patient-friendly and available in almost every healthcare facility. Therefore, we have focused on X-Ray images and developed an end-to-end deep learning model, i.e. Ensemble-CVDNet, to distinguish COVID-19 pneumonia from non-COVID pneumonia and healthy cases in this work. The proposed model is based on a combination of three lightweight pre-trained models SqueezeNet, ShuffleNet, and EfficientNet-B0 at different depths, and combines feature maps in different abstraction levels. In the proposed end to-end model, networks are used as feature extractors in parallel after fine-tuning, and some additional layers are used at the top of them. The proposed model is evaluated in the COVID-19 Radiography Database, a public data set consisting of 219 COVID-19, 1341 Healthy, and 1345 Viral Pneumonia chest X-Ray images. Experimental results show that our lightweight Ensemble-CVDNet model provides 98.30% accuracy, 97.78% sensitivity, and 97.61% F1 score using only 5.62M parameters. Moreover, it takes about 10ms to process and predict an X-Ray image using the proposed method using a mid level GPU. We believe that the method proposed in this study can be a helpful diagnostic screening tool for radiologists in the early diagnosis of the disease.
