Affiliations: [a] Research Lab: Smart systems for Engineering and E-health based on Technologies of Image and Telecomunications (SETIT), ISBS, Sfax University, Tunisia | [b] Center for Research on Microelectronics and Nanotechnology (CRMN), Sousse Technopole, Sahloul Sousse, Tunisia | [c] RIADI Laboratory, ENSI, Manouba University, Campus Universitaire de la Manouba, La Manouba, Tunisia | [d] Department IT, Higher Institute of Computer Science and Mathematics of Monastir, Tunisia | [e] Department IT, Higher Institute of Computer Science and Mathematics of Monastir, Tunisia
Correspondence:
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Corresponding author: Ridha Azizi, Research Lab: Smart systems for Engineering and E-health based on Technologies of Image and Telecomunications (SETIT), ISBS. Sfax University, Tunisia. E-mail: [email protected].
Abstract: In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.
