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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}, }
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