<![CDATA[Brain tumor classification remains a critical challenge in medical imaging because manual diagnosis from Magnetic Resonance Imaging is time-consuming and may produce inconsistent interpretations. Automated approaches using deep learning have shown promising results, although single-model methods may still face limitations in generalization and stability. This study introduces a lightweight multi model Convolutional Neural Network that combines MobileNetV2 and ResNet50V2 as dual-backbone feature extractors. Mo-bileNetV2 supports computational efficiency, while ResNet50V2 strengthens residual feature learning. The Bangladesh Brain MRI Dataset, which contains 6,056 images in three categories, Brain Glioma, Brain Menin-gioma, and Brain Tumor, was used in this study. All images were resized to 224 × 224 pixels before feature extraction, fusion, and classification. The proposed multi-model achieved 99.56% training accuracy and 93.37% validation accuracy, outperforming MobileNetV2 with 98.37% and 89.60 percent, and ResNet50V2 with 97.55% and 86.17 percent. On the test set, it reached 94.89% accuracy, 0.1536 loss, and 0.991 ROC AUC. These results show that integrating lightweight and deep architectures can improve robustness and accuracy while maintaining efficiency, making this approach suitable for real-world clinical support in brain tumor diagnosis.]]>
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