Classification of Red Blood Cells in the Kendall Space of Reflection Shapes
The classification of red blood cells (RBCs) or erythrocytes into three categories based on their shape, normal, sickle-shaped, and those with other deformations, has proven to be a crucial tool in diagnosing and managing sickle cell disease (SCD). Manual classification techniques have evolved into...
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MDPI AG
2024-12-01
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| author | Ximo Gual-Arnau Lluïsa Gual-Vayà |
| author_facet | Ximo Gual-Arnau Lluïsa Gual-Vayà |
| author_sort | Ximo Gual-Arnau |
| collection | DOAJ |
| description | The classification of red blood cells (RBCs) or erythrocytes into three categories based on their shape, normal, sickle-shaped, and those with other deformations, has proven to be a crucial tool in diagnosing and managing sickle cell disease (SCD). Manual classification techniques have evolved into automated tools, with numerous classification methods being applied based on different ways of representing the cells. In this work, we propose a novel methodology for representing RBCs, defined by selecting <i>k</i> landmarks along the cell boundaries and characterizing shapes as points in the Kendall space of reflection shapes, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mo>Ω</mo><mn>2</mn><mi>k</mi></msubsup></semantics></math></inline-formula>. Using this representation, we applied an embedding of the Kendall space <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mo>Ω</mo><mn>2</mn><mi>k</mi></msubsup></semantics></math></inline-formula> into a Euclidean space, which allowed for the use of machine learning classification algorithms. We also compared our results with those obtained using other classification methods applied to the same dataset in the literature, highlighting the strong performance of our approach in terms of classification accuracy. |
| format | Article |
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| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Mathematical and Computational Applications |
| spelling | doaj-art-ef8eab6774fc49f0aebfbf67a40f1cc12025-08-20T02:00:35ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472024-12-0129612210.3390/mca29060122Classification of Red Blood Cells in the Kendall Space of Reflection ShapesXimo Gual-Arnau0Lluïsa Gual-Vayà1Departament de Matemàtiques, Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castelló, SpainDepartament de Matemàtiques, Institute of Mathematics and Applications, Universitat Jaume I, 12071 Castelló, SpainThe classification of red blood cells (RBCs) or erythrocytes into three categories based on their shape, normal, sickle-shaped, and those with other deformations, has proven to be a crucial tool in diagnosing and managing sickle cell disease (SCD). Manual classification techniques have evolved into automated tools, with numerous classification methods being applied based on different ways of representing the cells. In this work, we propose a novel methodology for representing RBCs, defined by selecting <i>k</i> landmarks along the cell boundaries and characterizing shapes as points in the Kendall space of reflection shapes, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mo>Ω</mo><mn>2</mn><mi>k</mi></msubsup></semantics></math></inline-formula>. Using this representation, we applied an embedding of the Kendall space <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mo>Ω</mo><mn>2</mn><mi>k</mi></msubsup></semantics></math></inline-formula> into a Euclidean space, which allowed for the use of machine learning classification algorithms. We also compared our results with those obtained using other classification methods applied to the same dataset in the literature, highlighting the strong performance of our approach in terms of classification accuracy.https://www.mdpi.com/2297-8747/29/6/122erythrocytesKendall spacemachine learning algorithmsshape classification |
| spellingShingle | Ximo Gual-Arnau Lluïsa Gual-Vayà Classification of Red Blood Cells in the Kendall Space of Reflection Shapes Mathematical and Computational Applications erythrocytes Kendall space machine learning algorithms shape classification |
| title | Classification of Red Blood Cells in the Kendall Space of Reflection Shapes |
| title_full | Classification of Red Blood Cells in the Kendall Space of Reflection Shapes |
| title_fullStr | Classification of Red Blood Cells in the Kendall Space of Reflection Shapes |
| title_full_unstemmed | Classification of Red Blood Cells in the Kendall Space of Reflection Shapes |
| title_short | Classification of Red Blood Cells in the Kendall Space of Reflection Shapes |
| title_sort | classification of red blood cells in the kendall space of reflection shapes |
| topic | erythrocytes Kendall space machine learning algorithms shape classification |
| url | https://www.mdpi.com/2297-8747/29/6/122 |
| work_keys_str_mv | AT ximogualarnau classificationofredbloodcellsinthekendallspaceofreflectionshapes AT lluisagualvaya classificationofredbloodcellsinthekendallspaceofreflectionshapes |