Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients’ condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection.

中文翻译：

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基于时空信息融合的COVID-19 CT扫描计算机辅助诊断


冠状病毒病（COVID-19）具有高度传染性和致病性。目前，COVID-19的诊断基于核酸检测，但存在假阴性和滞后现象。使用肺部CT扫描可以帮助筛查和有效监测确诊病例。计算机辅助诊断技术的应用可以减轻医生的负担，有利于快速、大规模的诊断筛查。在本文中，我们提出了一种基于时空信息融合的COVID-19自动检测方法。利用深度学习方法中的分割网络对肺部区域和病灶区域进行分割，提取多张CT扫描的时空信息特征进行辅助诊断分析。该方法的性能在收集的数据集上得到了验证。我们实现了COVID-19 CT扫描和非COVID-19 CT扫描的分类，并通过CT扫描分析患者病情的发展。平均准确率为96.7%，灵敏度为95.2%，F1评分为95.9%。每次扫描大约需要 30 秒进行检测。