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This is a Cohort Study study (also classified as: Experimental Study).

October 15, 2025ILKOM Jurnal IlmiahOpen Access

Performance Comparison of Ensemble Learning Models for Brain Tumor Detection on Augmented MRI Datasets

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Machine learning approaches improve brain tumor diagnosis accuracy in MRI scans, suggesting effective detection methods for gliomas and meningiomas.

Random Forest achieved the highest accuracy of 72% in detecting brain tumors, supported by a robust ROC-AUC of 0.91.
Image segmentation using the U-Net model enhanced medical image analysis by integrating spatial and contextual data effectively.
Data augmentation techniques improved the dataset from 7,023 to 8,000 images, addressing class balance and potential overfitting issues.
Ensemble algorithms like Random Forest and XGBoost illustrate the power of machine learning in automating brain tumor detection.

Titaley et al. (2025) studied this question.

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Machine learning approaches improve brain tumor diagnosis accuracy in MRI scans, suggesting effective detection methods for gliomas and meningiomas.