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<p>This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from CGR images, we constructed a state space that effectively distinguishes different species and variants. Our method achieved <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_98366_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="39" /> </InlineMediaObject> <EquationSource Format="TEX">\(100\%\)</EquationSource> </InlineEquation> accuracy in species classification, with a notable <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_98366_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">\(76\%\)</EquationSource> </InlineEquation> accuracy for SARS-CoV-2 variants despite their genetic similarities. Using a Support Vector Machine (SVM) as a classifier further enhanced the performance. This approach, which requires fewer steps than most existing methods, offers an efficient and effective tool for viral classification, with implications for bioinformatics, public health, and vaccine development.</p>

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Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月29日

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  • J. P. Correia,
  • L. R. da Silva,
  • R. Silva

摘要

This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from CGR images, we constructed a state space that effectively distinguishes different species and variants. Our method achieved \(100\%\) accuracy in species classification, with a notable \(76\%\) accuracy for SARS-CoV-2 variants despite their genetic similarities. Using a Support Vector Machine (SVM) as a classifier further enhanced the performance. This approach, which requires fewer steps than most existing methods, offers an efficient and effective tool for viral classification, with implications for bioinformatics, public health, and vaccine development.