Computed Tomography (CT) scans provide a detailed image of the lungs, allowing clinicians to observe the extent of damage caused by COVID-19. The CT severity score (CTSS) of COVID-19 can be categorized based on the extent of lung involvement observed on a CT scan. This paper proposes a domain knowledge-based pipeline to extract the infection regions using diverse image-processing algorithms and a pre-trained UNET model. An ensemble of three machine-learning models, Random Forest (RF), Extremely Randomized Trees (ERT), and Support Vector Machine (SVM), is employed to classify the CT scans into different severity classes. The proposed system achieved a macro F1 score of 57.47% on the validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition (AI-MIA-COV19D).

翻译：计算机断层扫描（CT）可提供肺部详细影像，使临床医生能够观察COVID-19造成的损伤程度。COVID-19的CT严重性评分（CTSS）可根据CT扫描中观察到的肺部受累范围进行分级。本文提出一种基于领域知识的处理流程，利用多种图像处理算法和预训练的UNET模型提取感染区域。采用随机森林（RF）、极端随机树（ERT）和支持向量机（SVM）三种机器学习模型的集成方法，将CT扫描分类为不同严重性等级。在AI赋能医学图像分析研讨会与COVID-19诊断竞赛（AI-MIA-COV19D）的验证数据集上，所提系统实现了57.47%的宏平均F1分数。