利用基因组图像处理技术检测COVID-19

M. Hammad, V. F. Ghoneim, M. Mabrouk
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引用次数: 2

摘要

2019年新型冠状病毒病(COVID-19)是2019年3月底在中国武汉市出现的一种新的大流行,影响了全球数百万人。COVID-19是由新型严重急性呼吸综合征冠状病毒2 (SARSCoV-2)流行引起的。此外,在过去二十年中,已经列出了几种病毒流行病,如中东呼吸综合征冠状病毒(MERSCoV)和严重急性呼吸综合征冠状病毒1 (SARSCoV-1),它们分别导致中东呼吸综合征和SARS疾病。由于它们的遗传相似性,检测这些病毒流行病是一个困难的问题。本文利用基因组图像处理技术,开发了一套有效的自动化系统,利用病毒的全基因组序列对这些病毒流行病进行分类,以方便诊断,并在短时间内提高检测精度。使用两个分类器:SVM和KNN,结果达到了100%的总体准确率。然而,KNN分类器在执行时间性能上优于SVM。
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Detection of COVID-19 Using Genomic Image Processing Techniques
Novel Coronavirus Disease 2019 (COVID-19) is a new pandemic that appeared at the end of March 2019 in Wuhan city, China, which affected millions worldwide. COVID-19 is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) epidemic. Also, several viral epidemics have been listed in the last two decades, like the middle east respiratory syndrome coronavirus (MERSCoV) and the severe acute respiratory syndrome coronavirus 1 (SARSCoV-1), which cause MERS, and SARS diseases, respectively. Detection of these viral epidemics is a difficult issue because of their genetic similarity. In this paper, an effective automated system was developed to classify these viral epidemics using their complete genomic sequences via the genomic image processing techniques to facilitate the diagnosis and increase the detection accuracy in a short time. Results achieved an overall accuracy of 100% using two classifiers: SVM and KNN. However, the KNN classifier shows a privilege over the SVM in the execution time performance.
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