<![CDATA[Magnetic Resonance Imaging (MRI) plays a vital role in the early detection of brain tumors. However, standard Convolutional Neural Network (CNN) models often struggle to extract truly relevant features from complex MRI structures. This limitation creates a gap in achieving robust and clinically interpretable classifications, as feature redundancy and weak attention toward tumor-specific regions may reduce diagnostic reliability. To address this gap, this study introduces a Neural Dynamic Network (NDN) that integrates EfficientNetV2S with a dynamic attention-based mechanism to adaptively highlight informative features while suppressing noise. The proposed model was evaluated using a 5-fold cross-validation scheme and tested on unseen data. Compared with the baseline CNN, the NDN consistently demonstrated higher accuracy, precision, recall, and F1-score across folds and final testing, reflecting improved robustness and balanced sensitivity. NDN yielded significant improvements, with the 5-fold validation averaging an accuracy of 88.44%, a precision of 87.84%, a recall of 87.88%, and an F1-score of 87.82%. Beyond numerical performance, interpretability analysis utilizing Grad-CAM demonstrated that NDN generates more concentrated and clinically consistent heatmaps. In contrast, the baseline CNN produced dispersed activations that exhibited less alignment with tumor regions. Overall, the findings confirm that incorporating a dynamic attention-based mechanism substantially enhances both feature selection and visual interpretability. This makes the NDN architecture more reliable for MRI-based brain tumor classification and highly suitable as a decision-support tool in clinical workflows.]]>
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