Random Oversampling Bagging Ensemble Model for Leukemia Classification

  • Unique Paper ID: 175819
  • Volume: 11
  • Issue: 11
  • PageNo: 4593-4597
  • Abstract:
  • In medical research, Digital Image Processing plays a crucial role encompassing image acquisition, contrast enhancement, image segmentation, feature extraction, and classification. During the feature extraction phase, the Gray Level Co-occurrence Matrix (GLCM), along with statistical and geometrical features from blood smear images, are extracted to form a feature vector. These extracted features are then used for leukemia prediction and classification. A Support Vector Machine (SVM) can be employed for prediction, while a Random Oversampling-based Bagged Ensemble method will be utilized for final classification. The performance of the proposed classification model should be evaluated using metrics such as accuracy, precision, and recall.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{175819,
        author = {Mariena A A},
        title = {Random Oversampling Bagging Ensemble Model for Leukemia Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4593-4597},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175819},
        abstract = {In medical research, Digital Image Processing plays a crucial role encompassing image acquisition, contrast enhancement, image segmentation, feature extraction, and classification. During the feature extraction phase, the Gray Level Co-occurrence Matrix (GLCM), along with statistical and geometrical features from blood smear images, are extracted to form a feature vector. These extracted features are then used for leukemia prediction and classification. A Support Vector Machine (SVM) can be employed for prediction, while a Random Oversampling-based Bagged Ensemble method will be utilized for final classification. The performance of the proposed classification model should be evaluated using metrics such as accuracy, precision, and recall.},
        keywords = {Classification, Ensemble Model, Random Oversampling.},
        month = {April},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 4593-4597

Random Oversampling Bagging Ensemble Model for Leukemia Classification

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