Comprehensive Approach to Covid-19 Detection and Severity Assessment

Abstract

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has significantly impacted global health systems, economies, and societies worldwide. Rapid and accurate detection of COVID-19 infections, as well as the timely assessment of disease severity, are crucial for effective disease management, resource allocation, and public health interventions. This study presented a two-step methodology for identifying the presence of COVID-19 infection in lung CT scans and assessing the severity of the patient's condition. Feature extraction is performed by utilizing pre-trained models, and the features from AlexNet, DenseNet-201, and ResNet-50 are combined through analysis. The identification of COVID-19 is conducted by the utilization of an Artificial Neural Network (ANN) model. Once the COVID-19 infection is diagnosed, a process of assessing the severity of the infection is carried out. To do this, visual characteristics are integrated with the clinical data and categorized using Cubic Support Vector Machine (SVM) into three categories: High, Moderate, and Low. Patients with a high risk might receive increased attention by categorizing them into three severity levels.