An Explainable Transfer Learning Framework Based on EfficientNetB3 for Multi-Class Brain Tumor Classification from MRI Images

Accurate and interpretable brain tumor classification from magnetic
resonance imaging (MRI) remains challenging due to substantial
variations in tumor appearance and the limited interpretability of
deep learning models. This paper presents an explainable transfer
learning framework based on EfficientNetB3 for four-class brain tumor
classification, covering Glioma, Meningioma, Pituitary Tumor, and No
Tumor. The framework combines ImageNet-based transfer learning,
training-time data augmentation, selective backbone fine-tuning with
frozen Batch Normalization layers, and a customized classification
head with regularization. The model is evaluated on 7,153 MRI images
using a stratified 70%/15%/15% train-validation-test split. On the
held-out test set, the proposed framework achieves 99.16% accuracy,
with weighted precision, recall, and F1-score of 99.17%, 99.16%, and
99.16%, respectively. Only nine misclassifications are observed among
1,073 test images. Furthermore, Grad-CAM visualizations provide
qualitative insights into the image regions contributing to the model’s
predictions. The results demonstrate that the proposed framework
combines high multi-class classification performance with
model-level interpretability, providing a promising foundation for
explainable AI-assisted brain tumor analysis.