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) based scoring method is used to identify the extent of lung involvement observed on a CT scan. This paper presents a domain knowledge-based pipeline for extracting regions of infection in COVID-19 patients using a combination of image-processing algorithms and a pre-trained UNET model. The severity of the infection is then classified into different categories using an ensemble of three machine-learning models: Extreme Gradient Boosting, Extremely Randomized Trees, and Support Vector Machine. The proposed system was evaluated on a validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition (AI-MIA-COV19D) and achieved a macro F1 score of 64%. These results demonstrate the potential of combining domain knowledge with machine learning techniques for accurate COVID-19 diagnosis using CT scans. The implementation of the proposed system for severity analysis is available at \textit{https://github.com/aanandt/Enhancing-COVID-19-Severity-Analysis-through-Ensemble-Methods.git }

翻译：计算机断层扫描（CT）可提供肺部详细图像，使临床医生能够观察COVID-19造成的损伤程度。基于CT严重程度评分（CTSS）的评分方法用于识别CT扫描中观察到的肺部受累程度。本文提出了一种基于领域知识的流程，通过结合图像处理算法和预训练UNET模型，提取COVID-19患者的感染区域。随后，利用三种机器学习模型的集成方法——极端梯度提升、极端随机树和支持向量机——将感染严重程度划分为不同类别。该系统在人工智能医学图像分析研讨会及COVID-19诊断竞赛（AI-MIA-COV19D）的验证数据集上进行了评估，实现了64%的宏F1分数。这些结果证明了将领域知识与机器学习技术相结合，利用CT扫描准确诊断COVID-19的潜力。本研究所提出的严重程度分析系统实现代码可在\textit{https://github.com/aanandt/Enhancing-COVID-19-Severity-Analysis-through-Ensemble-Methods.git}获取。