THE EFFECT OF FEATURE EXTRACTION ON COVID-19 CLASSIFICATION

Abstract

X-ray imaging stands as a prominent technique for diagnosing COVID-19, and it also serves as a crucial tool in the medical field for the analysis of various diseases. Numerous approaches are available to facilitate this analysis. Among these techniques, one involves the utilization of a Feature Extractor, which effectively captures pertinent characteristics from X-ray images. In a recent study, a comprehensive examination was conducted using 25 distinct feature extractors on X-ray images specific to COVID-19 cases. These images were categorized into two classes: COVID-19-positive and non-COVID-19. To enable a thorough evaluation, a sequence of machine learning classifiers was employed on these categorized images. The outcomes derived from this experimentation gauged the magnitude of impact that each individual feature exerted on COVID-19-related imagery. This assessment aimed to determine the efficacy levels of various feature extractors in terms of detection capability. Consequently, a distinction emerged between the more effective and less effective feature extractors, shedding light on their varying degrees of contribution to the detection process. Moreover, the comparative performance of different classifiers became evident, revealing the classifiers that exhibited superior performance when measured against their counterparts.