ENSEMBLE METHODS FOR ENHANCED COVID-19 CT SCAN SEVERITY ANALYSIS

Computed Tomography (CT) scans provide a high-resolution image of the lungs, allowing clinicians to identify the severity of infections in COVID-19 patients. This paper presents a domain knowledge-based pipeline for extracting infection regions from COVID-19 patients using a combination of image-processing algorithms and a pre-trained UNET model. Then, an infection rate-based feature vector is generated for each CT scan. The infection severity is then classified into four categories using an ensemble of three machine-learning models: Random Forest, Support Vector Machines, and Extremely Randomized Trees. The proposed system is evaluated on the validation and test datasets with a macro F1 score of 58% and 46.31%, respectively. Our proposed model has achieved 3rd place in the severity detection challenge as part of the IEEE ICASSP 2023: AI-enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition (AI-MIACOV19D). The implementation of the proposed system is available at https://github.com/aanandt/Enhancing-COVID19-Severity-Analysis-through-Ensemble-Methods.git