Automated Identification and Categorization of COVID-19 via X-Ray Imagery Leveraging ROI Segmentation and CART Model

Abstract

COVID-19, a novel disease first identified in China in December 2019, has rapidly precipitated a global pandemic, impacting public health and the global economy with unprecedented severity. Accurate detection of this virus is of paramount importance, yet current methodologies present significant limitations and challenges. Polymerase Chain Reaction (PCR) diagnostic kits, a commonly utilized detection method, often yield false-negative results. Moreover, the recent strains of the virus elude detection solely by PCR testing. In contrast, imaging techniques such as chest X-rays or Computerized Tomography (CT) scans offer radiologists a higher degree of diagnostic accuracy. However, the vast quantity of required imaging coupled with a shortage of radiologists has underscored the necessity for automated detection methods. This study proposes an integrated system for the automated detection and classification of COVID-19 infection. By utilizing an amalgamation of computer vision tools and machine learning algorithms, this system aims to provide clinicians with rapid and accurate diagnoses without the need for human intervention. This paper, therefore, presents an advancement in the use of medical imaging for the detection and classification of COVID-19, offering a potential solution to the current limitations in testing capabilities.