Volume 9 • Issue 2 • PP: 31–40 • 2025
Multi-Classification of Brain Tumor MRI Images Hybrid VGG16 Support Vector Machine Model
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This article was handled by an independent editor. The author serving on the Editorial Board had no involvement in the peer-review process or the final editorial decision.
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Brain tumor research is essential for timely detection and appropriate treatment. Diagnosis is challenging because tumors vary in morphology, size, location, surface texture, and visual appearance across medical images. This study proposes a combined method for brain tumor detection from MRI images using three publicly available datasets containing 3,966 T1-weighted contrast-enhanced magnetic resonance images (T1-w MRI), classified as glioma, meningioma, pituitary tumor, or no tumor. The diagnostic pipeline applies preprocessing and data augmentation to improve image quality and variability. It then uses a VGG16 deep convolutional neural network to extract features and a Support Vector Machine (SVM) classifier to predict the class. The modified VGG16 output is supplied to the SVM, producing a test accuracy of 97.2%. Results from XGBoost, AdaBoost, Decision Tree, and K-Nearest Neighbors classifiers show that the SVM endpoint classifier performs best among the evaluated alternatives.
Keywords
References
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