<![CDATA[Accurate brain tumour identification from magnetic resonance imaging (MRI) requires models that can effectively localize tumour regions while maintaining high classification performance and computational efficiency. This study presents an integrated U-Net–MobileNetV2 framework for automated brain tumour analysis using MRI images. The proposed pipeline incorporates image thresholding, morphological processing, contour-based cropping, bicubic resizing, normalization, and data augmentation prior to model training. U-Net is employed to segment tumour regions and preserve diagnostically relevant spatial information, while MobileNetV2 performs lightweight classification using segmentation-guided representations. The dataset consists of 512 × 512-pixel brain MRI images divided into 80% training, 10% validation, and 10% testing subsets. Model performance is evaluated using accuracy, precision, recall, and F1-score and compared with CNN, Random Forest, Naïve Bayes, and Support Vector Machine classifiers. The proposed framework achieves 99.52% accuracy, 97.57% precision, 98.05% recall, and 97.81% F1-score, outperforming all evaluated baseline models. Compared with the strongest baseline, SVM, the proposed method improves accuracy by 1.43 percentage points. These results demonstrate that combining segmentation-driven tumour localization with lightweight deep feature learning provides an effective framework for accurate and computationally efficient MRI-based brain tumour classification.]]>
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