Towards Hybrid Approach Based SVM and Radiomics Features for COVID-19 Classification and Segmentation

Radiological chest exams, such as chest X-rays, are critical in the fight against the COVID-19 pneumonia outbreak, which is caused by the coronavirus strain SARS-Cov-2. This study looks into classification models to distinguish chest X-ray images based on Radiomics features in order to understand the unique radiographic characteristics of COVID-19. This study used datasets consisting of 136 segmented chest X-rays to train and test the categorization algorithms. Using the Pyradiomics collection, first and second-order statistical texture characteristics were retrieved from the right (R), left (L), superior, middle, and bottom lung zones for each lung side. For feature selection, data was separated into training (80%) and test (20%) groups. The most relevant Radiomics features were picked after considering their relevance and confirmation accuracy. Support vector machines (SVM) were evaluated as suitable classifiers using a lung segmentation-based grey level pixel model (AUC = 83.7%). Our findings indicate a preference for the upper lung zone, as well as a preference for Radiomics feature selection in the right lung. In the future, we will focus on COVID-19 categorization and segmentation for more precise forecasting utilizing a hybrid technique based on SVM and Radiogenomics characteristics.