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Detection of COVID-19 Cases using Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 52 |
| Year of Publication: 2022 |
| Authors: Reda Elbarougy, G.M. Behery, Y.M. Younes, Esmail Aboghrara |
10.5120/ijca2022921943
|
Reda Elbarougy, G.M. Behery, Y.M. Younes, Esmail Aboghrara . Detection of COVID-19 Cases using Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) Classifier. International Journal of Computer Applications. 183, 52 ( Feb 2022), 40-44. DOI=10.5120/ijca2022921943
Abstract
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. The early discovery of COVID-19 out of precise analysis, specifically in circumstances where there are no immediately visible symptoms,could lower the patient's risk of death. 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. A machine learning (ML) strategy for recognizing COVID-19 in chest x-ray images is proposed in this paper.Features were extracted using the histogram-oriented gradient (HOG) from x-ray images. The classification performance of the support vector machine (SVM) classifier used in this study was excellent. The proposed HOG feature technique provided high accuracy reach (96.6%).
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