Brain Tumor Classification in Magnetic Resonance Imaging Using Machine Learning Techniques
DOI:
https://doi.org/10.5753/reic.2026.7814Keywords:
Brain tumors, Magnetic resonance imaging, Machine learning, Random Forest, Medical image classificationAbstract
This study investigated the application of machine learning techniques for the automatic classification of brain tumors from magnetic resonance imaging (MRI) scans to support medical diagnosis. The images underwent preprocessing, including data augmentation (rotations, flips, and brightness adjustments), feature extraction and normalization, as well as dimensionality reduction to retain the most relevant information. Two machine learning algorithms, Random Forest and Support Vector Machine (SVM), were evaluated and compared under different experimental scenarios. Random Forest achieved the best overall performance, reaching approximately 98% classification accuracy while producing fewer false negatives, a critical aspect in clinical applications. SVM also demonstrated satisfactory performance, with an accuracy of approximately 94%, although it was more sensitive to data variations. Overall, the findings highlight the potential of machine learning as a valuable tool for supporting medical image analysis and improving the diagnosis of brain tumors.
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