aspg-28-3860-1792156237 20261016131037 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 9 2 Asifa Iqbal School of international languages Zhengzhou University, Henan, China 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. 2025 31 40 3860 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.090204 https://www.americaspg.com/journal/28/article/3860 https://www.americaspg.com/storage/articles/manuscripts/01M4Y640YMQZKYTKJCZB5TMJ36.pdf