Dynamic Attention-Augmented Neural Network for Accurate and Efficient Brain Tumor Classification and Segmentation Using MRI

Magnetic Resonance Imaging (MRI) classification and segmentation of brain tumors are still an important problem in medical imaging because tumors are typically heterogeneous and multicellular. Old-fashioned diagnostics do not scale, generalize or compute efficiently, and hence do not apply clinically. In order to overcome these problems, the research introduces a new Dynamic Attention-Augmented Neural Network (DAANN) that will improve the classification accuracy, robustness and computational efficiency of brain tumors. The proposed DAANN features a dynamic attention system that enables it to choose the diagnostically relevant areas from MRI images automatically, enabling it to perform even in noisy or sparse data. Combined with feature extraction over time, the model captures morphological and sequential dependencies to render full tumor analysis. When trained on benchmark MRI images, the DAANN had a classification precision of 94.8% and topped models like CNN and BiLSTM. The model was also robust to noise — with 89.0% accuracy under moderate noise levels — and operated efficiently using very little training data, with 78.0% accuracy on 10% of the training set. These findings point to the DAANN’s real-time clinical availability in resource-constrained settings. As it takes up the challenge of what is possible, this work adds value to medical imaging as it provides a scalable and reproducible way to diagnose brain tumors. The work in the future will be to extend the model to multi-organ classification and to make it more easily read by clinical decision makers.