We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select one or multiple sets of a fixed number of slice images for training and validation. A 3D CNN network with BERT is used to classify this set of selected slice images. In this network, an embedding feature is also extracted. In cases where there are more than one set of slice images in a volume, the features of all sets are extracted and pooled into a global feature vector for the whole CT-scan volume. A simple multiple-layer perceptron (MLP) network is used to further classify the aggregated feature vector. The models are trained and evaluated on the provided training and validation datasets. On the validation dataset, the accuracy is 0.9278 and the F1 score is 0.9261.

翻译：我们从肺部CT扫描切片图像中展示了自动的COVID1-19诊断框架。 在这个框架内,CT扫描卷的切片图像首先使用分离技术进行预处理,以过滤封闭肺的图像,并去除无用背景。然后使用再抽样方法选择一组或数组固定切片图像来进行培训和验证。使用有3DCNN的网络和BERT对这组选定的切片图像进行分类。在这个网络中,还提取了一个嵌入特性。如果一个体积中有一组以上的切片图像,所有各组的特征都被提取出来,并汇集到一个全CT扫描卷的全球地貌矢量中。使用一个简单的多层透视器网络来进一步分类综合地貌矢量。这些模型在所提供的培训和验证数据集中接受培训和评价。在验证数据集中,精确度为 0.9278, F1评分为 0.9261。