Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques

Obtaining a chest x-ray image is one of the main clinical observations for screening novel coronavirus. Most patients with COVID-19 viral pneumonia have abnormalities on a chest x-ray, such as consolidation. Computer vision-based solutions are a viable option for improving COVID-19 detection accuracy. However, Other classification models are presently in use in the healthcare industry. One such model uses radiographs to identify pneumonia cases and has attained a high enough level of accuracy to be applied to actual patients. This research assesses the advantages of employing various feature extraction strategies in order to improve the classification performance of the COVID-19 detection. The objective is to create a COVID-19 classifier using several feature extraction techniques, such as Fractal Descriptor (FD), Histogram Oriented Gradient (HOG), and Local Binary Pattern (LBP), using the pneumonia dataset as a base. Combining these feature extraction methods, an accuracy of 95% was attained utilizing FD.