  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
}
