Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification

Magnetic resonance imaging (MRI) is central to noninvasive brain tumor assessment, yet clinical uptake of artificial intelligence depends on both accuracy and transparency. This study presents a lightweight and interpretable hybrid framework that fuses features from two efficient convolutional backbones, MobileNetV2 and EfficientNetV2B0, using late fusion with global average pooling and vector concatenation. Classification is performed with a K‑Nearest Neighbors (KNN) head configured with k = 5, Euclidean distance, and distance‑based weighting. The dataset contains 7,023 MRI images drawn from Figshare, SARTAJ, and BR35H. Data were split with a 20% held‑out test set and a validation set equal to 20% of the remaining training pool, yielding 64%/16%/20% train/val/test. Four diagnostic categories were evaluated: Glioma, Meningioma, Pituitary, and Notumor. The confusion matrix shows a compact diagonal, and class‑wise precision, recall, and F1 are consistently high on the test set. A 5‑fold cross‑validation with normality assessment and paired significance testing supports robustness across folds. On the held‑out test set, class‑wise ROC-AUC was 1.00 for all four classes, and overall accuracy was 99.69%. Results should be interpreted in light of the unified dataset; external validation is warranted. Clinical interpretability is supported by class‑wise Grad‑CAM overlays and SHAP analyses, including waterfall plots that quantify individual feature contributions. These findings indicate that a dual‑backbone late‑fusion design coupled with a simple nonparametric classifier delivers strong, balanced performance while providing anatomically plausible case‑level insight into model decisions.