Robust Deep Transfer Learning Framework for Multi-Class Brain Tumor Classification with Explainable Decision Support

Accurate brain tumor identification from magnetic
resonance imaging (MRI) is essential for early clinical decision
support, yet reliable multi-class classification remains challenging
because tumor appearance varies across shape, texture, and
intensity patterns. This study proposes an explainable deep
ensemble framework for four-class brain tumor MRI classification, covering glioma, meningioma, pituitary tumor, and
no-tumor cases. The pipeline applies image preprocessing and
augmentation, evaluates multiple pretrained convolutional base-
lines, and combines complementary transfer-learning models
through weighted decision fusion to improve generalization.
Model performance is assessed using accuracy, precision, recall,
F1-score, bootstrap confidence estimation, and train-validation
stability analysis. Experimental results on the Kaggle brain tumor
MRI dataset show that the proposed framework achieves a
verified accuracy of 99.44%, with precision, recall, and F1-score
above 0.994. Explainability analysis further supports the clinical
relevance of the learned decision regions. The results indicate
that ensemble-based transfer learning can provide robust and
interpretable MRI-based brain tumor screening support