A Dual Framework of Feature Selection based on the Fusion of HOG and Pyramid HOG for the Categorization of COVID-19
Keywords:
COVID, Classification, Entropy, HOG, PHOG, SVMAbstract
COVID-19 was initially detected in Wuhan, China. The virus spread all over the world at a rapid speed. COVID-19 is an infection that may cause infections in the respiratory system and the lungs. In order to diagnose COVID-19, chest X-rays have been utilized extensively. The purpose of this research is to create a computer-vision-based method for identifying COVID in chest X-rays. In the proposed model handcrafted features known as HOG and PHOG are derived and fused. After features have been fused, the Binary Grey Wolf Optimization technique is used with an Entropy-Based Optimization Algorithm to select the most significant features possible. The proposed model results are evaluated on a benchmark X-ray dataset that gives greater than 99% accuracy. The proposed model performed better compared to existing published works in this domain.
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