Cancer Classification Using Pattern Recognition and Computer Vision Techniques
The rapid advancement of DNA microarray technology has significantly contributed to the classification of various cancers, particularly leukemia. However, the high-dimensional nature of gene expression data presents challenges such as data noise and irrelevant features, leading to reduced prediction...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02002.pdf |
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| _version_ | 1841554698952245248 |
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| author | Haddou Bouazza Sara Haddou Bouazza Jihad |
| author_facet | Haddou Bouazza Sara Haddou Bouazza Jihad |
| author_sort | Haddou Bouazza Sara |
| collection | DOAJ |
| description | The rapid advancement of DNA microarray technology has significantly contributed to the classification of various cancers, particularly leukemia. However, the high-dimensional nature of gene expression data presents challenges such as data noise and irrelevant features, leading to reduced prediction accuracy. This study proposes a novel Hybrid Filter-Wrapper Gene Selection (HFWGS) method that integrates filter-based techniques (Signal-to-Noise Ratio, Correlation Coefficient, and ReliefF) with wrapper-based approaches to enhance feature selection for leukemia classification. Additionally, a Hybrid Statistical-Gene Voting (HSGV) approach was implemented to further refine classification accuracy. A comparative analysis of classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), demonstrated that the HFWGS method consistently improved classification performance, achieving 100% accuracy with a reduced subset of genes. The proposed methods provide an efficient framework for optimizing gene selection and improving diagnostic accuracy in leukemia, paving the way for more targeted therapeutic interventions. |
| format | Article |
| id | doaj-art-4a77d62a033841a5ba842b7260d37f92 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-4a77d62a033841a5ba842b7260d37f922025-01-08T10:58:54ZengEDP SciencesITM Web of Conferences2271-20972024-01-01690200210.1051/itmconf/20246902002itmconf_maih2024_02002Cancer Classification Using Pattern Recognition and Computer Vision TechniquesHaddou Bouazza Sara0Haddou Bouazza Jihad1LAMIGEP, EMSILAMIGEP, EMSIThe rapid advancement of DNA microarray technology has significantly contributed to the classification of various cancers, particularly leukemia. However, the high-dimensional nature of gene expression data presents challenges such as data noise and irrelevant features, leading to reduced prediction accuracy. This study proposes a novel Hybrid Filter-Wrapper Gene Selection (HFWGS) method that integrates filter-based techniques (Signal-to-Noise Ratio, Correlation Coefficient, and ReliefF) with wrapper-based approaches to enhance feature selection for leukemia classification. Additionally, a Hybrid Statistical-Gene Voting (HSGV) approach was implemented to further refine classification accuracy. A comparative analysis of classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), demonstrated that the HFWGS method consistently improved classification performance, achieving 100% accuracy with a reduced subset of genes. The proposed methods provide an efficient framework for optimizing gene selection and improving diagnostic accuracy in leukemia, paving the way for more targeted therapeutic interventions.https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02002.pdf |
| spellingShingle | Haddou Bouazza Sara Haddou Bouazza Jihad Cancer Classification Using Pattern Recognition and Computer Vision Techniques ITM Web of Conferences |
| title | Cancer Classification Using Pattern Recognition and Computer Vision Techniques |
| title_full | Cancer Classification Using Pattern Recognition and Computer Vision Techniques |
| title_fullStr | Cancer Classification Using Pattern Recognition and Computer Vision Techniques |
| title_full_unstemmed | Cancer Classification Using Pattern Recognition and Computer Vision Techniques |
| title_short | Cancer Classification Using Pattern Recognition and Computer Vision Techniques |
| title_sort | cancer classification using pattern recognition and computer vision techniques |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_02002.pdf |
| work_keys_str_mv | AT haddoubouazzasara cancerclassificationusingpatternrecognitionandcomputervisiontechniques AT haddoubouazzajihad cancerclassificationusingpatternrecognitionandcomputervisiontechniques |