Comparative Performance of VGG16 and EfficientNetB0-Based Transfer Learning for Brain Tumor Classification

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average Pooling (GAP) layer for EfficientNetB0. To optimize the discriminative power of these static features, this study implements an ensemble-fusion framework, specifically Voting and Stacking classifiers, to integrate the strengths of diverse base learners (SVM, Random Forest, and XGBoost) in identifying complex brain tumor patterns. Results demonstrate that VGG16-based models consistently and significantly outperform EfficientNetB0-based models across all evaluation metrics. The