Abstract
基于肺部CT(computed tomography)影像的人工智能诊断是针对新型冠状病毒肺炎(coronavirus disease 2019，COVID-19)的有效辅助诊断方法之一。使用神经网络以及数字图像处理等技术，设计了一个基于切片内和切片间注意力机制的轻量级COVID-19分类模型，在此基础上开发了集早期筛查、病变评估、病灶分割功能和肺部及病灶像素分布直方图等功能于一体的COVID-19智能诊断系统。通过从武汉大学人民医院采集了247名COVID-19病患、152名其他肺炎患者和92名健康者的肺部CT图像，并制作为训练数据集用于网络训练。实验结果显示，提出的方法在验证集上的筛查任务和病变评估任务上的准确率分别达到88.63%和89.65%，算法模型中每人平均诊断时间缩短到0.4 s，系统具有重要的应用价值。
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