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@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},
        }