DualXBrNet: A Hybrid Dual-Path Cross-Level Fusion Framework for Multiclass Brain Tumor Classification Using MRI Images

Brain tumor classification from magnetic resonance imaging (MRI) can support computer-aided diagnosis by enabling automated differentiation of tumor and non-tumor categories. This study presents DualXBrNet, a hybrid dual-level fusion framework for multiclass brain tumor classification using the original BRISC2025 dataset. Three pretrained convolutional neural networks (CNNs), ResNet50V2, MobileNetV2, and EfficientNetB0, were optimized to learn complementary representations of glioma, meningioma, pituitary tumor, and no-tumor MRI images. Their deep features were integrated through a learnable Feature Fusion network, while the class probability outputs of the three CNNs and the Feature Fusion model were subsequently combined using a leakage-aware five-fold out-of-fold (OOF) decision-level stacking strategy. A low-complexity Logistic Regression meta-classifier was trained exclusively on the resulting 16-dimensional OOF probability representations to prevent information leakage during meta-learning. On the independent official test set, DualXBrNet achieved 96.80% accuracy and a macro-average ROC-AUC of 99.77%. Oracle, statistical, computational complexity, feature space, and Occlusion Sensitivity analyses were further conducted to assess model complementarity, computational efficiency, representation quality, and interpretability. The results demonstrate that feature-level fusion provides the primary performance improvement while OOF-based decision stacking offers complementary refinement within a leakage-aware evaluation protocol. Overall, the proposed framework emphasizes methodological rigor through leakage-aware meta-learning, independent testing, and comprehensive model analysis, rather than relying on classification accuracy alone.