Enhancing COVID-19 Severity Analysis through Ensemble Methods

• URL: http://arxiv.org/abs/2303.07130v2
• Date: Thu, 16 Mar 2023 08:59:24 GMT
• Title: Enhancing COVID-19 Severity Analysis through Ensemble Methods
• Authors: Anand Thyagachandran, Hema A Murthy
• Abstract summary: This paper presents a domain knowledge-based pipeline for extracting regions of infection in COVID-19 patients. The severity of the infection is then classified into different categories using an ensemble of three machine-learning models. The proposed system was evaluated on a validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition.
• Score: 13.792760290422185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 }

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