A Dual-Backbone Hybrid Deep Learning Framework with Squeeze-and-Excitation Attention and Grad-CAM Explainability for Multi-Class Brain Tumor MRI Classification

Accurate brain tumor classification from magnetic
resonance imaging (MRI) is essential for treatment planning,
yet manual interpretation remains time-consuming and subject to inter-observer variability. This paper proposes a dual-
backbone convolutional neural network that fuses EfficientNetB3
and MobileNetV2 features through a Squeeze-and-Excitation
(SE) channel-attention block for classifying glioma, meningioma,
pituitary tumor, and no-tumor MRI scans. The preprocessing
pipeline combines Gaussian–bilateral denoising with contrast-
limited adaptive histogram equalization (CLAHE) to suppress
noise while preserving diagnostically relevant contrast. The model
was trained progressively through frozen-backbone warm-up,
partial fine-tuning, and full end-to-end optimization with cosine
learning-rate scheduling on 7,153 MRI images. The proposed
network achieved 99.72% validation accuracy and 99.16% test
accuracy, with macro precision, recall, F1-score, and ROC-AUC
values of 0.9914, 0.9917, 0.9915, and 0.9997, respectively. Ablation
analysis showed that direct backbone fusion without SE attention
reached only 95.35% accuracy, whereas the complete attention-
enhanced model achieved 99.16%, confirming the importance
of adaptive channel recalibration. Grad-CAM was used to
visualize class-relevant tumor regions and improve prediction
transparency. These findings demonstrate that the proposed
attention-guided dual-backbone framework provides accurate,
interpretable, and computationally practical brain tumor classification for clinical decision-support applications.