Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 9 , Issue 2 , PP: 54-71, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Asifa Iqbal 1 *
Doi: https://doi.org/10.54216/JAIM.090204
Tumor brain research stands essential for detecting patients during timely periods and delivering proper treatment options. Inspecting tumors becomes difficult because tumor morphology shows diverse characteristics in terms of dimensions and placement surface texture patterns, and inconsistent visual features across various medical image types. A combined methodology will be implemented to detect brain tumors through MRI image analysis in this research. The model operated with three publicly accessible datasets containing 3,966 T1-weighted contrast-enhanced magnetic resonance images (T1-w MRI) that were split between glioma, meningioma, pituitary tumor and no tumor groups. The diagnosis pipeline starts by applying preprocessing and data augmentation steps that improve data quality alongside increasing its variability rates. The main structure of this system uses VGG16 deep convolutional neural network features alongside a Support Vector Machine (SVM) classifier to determine outputs. The modified VGG16 output became the SVM input, delivering optimal results while keeping the computational time sensible. The proposed hybrid model performs better than all existing methods analyzed in the literature according to experimental results. The test success rate of the model reached 97.2\%. Test outcomes from standard machine learning methods XGBoost, AdaBoost, Decision Tree, and K-Nearest Neighbors demonstrate that using SVM as the endpoint classifier boosts achievement levels in this dataset assessment.
Brain Tumors , Hybrid Model , Support Vector Machine , T1-w MRI , VGG16
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