The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.
【저자키워드】 COVID-19 detection, chest X-ray, deep features, CT-scan, Feature selection, Meta-heuristic, CGRO algorithm, 【초록키워드】 COVID-19, RT-PCR, virus, COVID, global pandemic, X-ray, Accuracy, Early detection, COVID-19 virus, WHO, dataset, World Health Organization, datasets, researcher, approach, feature, less, introduced, Ratio, Initially, 【제목키워드】 COVID-19, detection, deep, residual, Improved, Ratio,