Since the appearance of Covid-19 in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 3nd COV19D competition for Covid-19 Severity Prediction. In order to deal with the big gap between the validation and test results that were shown in the previous version of this competition, we proposed to combine the prediction of 2D and 3D CNN predictions. For the 2D CNN approach, we propose 2B-InceptResnet architecture which consists of two paths for segmented lungs and infection of all slices of the input CT-scan, respectively. Each path consists of ConvLayer and Inception-ResNet pretrained model on ImageNet. For the 3D CNN approach, we propose hybrid-DeCoVNet architecture which consists of four blocks: Stem, four 3D-ResNet layers, Classification Head and Decision layer. Our proposed approaches outperformed the baseline approach in the validation data of the 3nd COV19D competition for Covid-19 Severity Prediction by 36%.

翻译：自2019年底Covid-19出现以来,Covid-19成为人工智能(AI)界的一个积极研究课题。最有趣的AI专题之一是对医学成像的Covid-19分析。CT-scan成像是有关该疾病最信息的工具。这项工作是Covid-19 Severity 预测第三次COV19D竞争竞赛的一部分。为了解决本次竞争前一版显示的验证和测试结果之间的巨大差距,我们提议将2D和3DCNN预测的预测结合起来。关于2D CNN方法,我们提议2B-InpheptionResnet结构,它分别包括分裂肺和所有输入CT-scan的切片感染的两条路径。每一个路径都包括ConvLayer和Invition-Resnet在图像网络上预先训练的模式。关于3DCNN方法,我们提议混合-DecoVNet结构由四个区块组成:Stem、4 3D-ResNet层、分类头层和决策层。我们提议的C-19Sniversive Cnov 的基线方法超越了Snovive Driviewalmental 19 Cnoval 3Dviewal 3D。我们提议的Snov 319的Cndalviewmentalmentalviewd36的Cviewd 319的Cviewd方法。</s>