Random Oversampling Bagging Ensemble Model for Leukemia Classification

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Mariena A A

Random Oversampling Bagging Ensemble Model for Leukemia Classification

Authors: Mariena A A

Unique Paper ID: 175819
Volume: 11
Issue: 11
PageNo: 4593-4597

Keywords: Classification Ensemble Model Random Oversampling.

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.

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Cite This Article

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

Random Oversampling Bagging Ensemble Model for Leukemia Classification

Available:https://ijirt.org/article?manuscript=175819

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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