الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The COVID-19 coronavirus epidemic has spread rapidly worldwide after a person became infected with a severe health problem. The World Health Organization has declared the coronavirus a global threat (WHO). COVID-19 is now considered the most severe and fatal human illness caused by a novel coronavirus. The coronavirus which is considered to have originated in Wuhan, China spread fast around the world in December 2019 and caused a huge the number of fatalities.
Early detection of COVID 19, particularly in cases with no apparent symptoms, may reduce the patient’s mortality rate. The demand for supplemental diagnostic equipment has grown because there are no precise and available toolkits for automation. However, recent studies using radiological imaging techniques have revealed important information for detecting the COVID-19. Combining artificial intelligence and radiological imaging techniques can help improve disease recognition accuracy. COVID 19 detection using machine learning techniques will aid healthcare systems around the world in recovering patients more rapidly; therefore, this study proposed a machine vision method for detecting COVID-19 in x-ray images of the chest. The histogram-oriented gradient (HOG) and convolutional neural network (CNN) features extracted from x-ray images were fused and classified using support vector machine (SVM) and softmax. The proposed feature fusion technique (99.36 percent) outperformed individual feature extraction methods such as HOG (87.34 percent) and CNN (93.64 percent).