Detection of stage of lung changes in COVID?19 disease based on CT images: a radiomics approach

摘要

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, andabsorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people wereevaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the randomforest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposedmethod attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive,82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can beused for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time requiredto hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost ofcare and treatment for hospitalized individuals.