Abstract

Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced segmentation framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance segmentation performance. Unlike conventional U-Net-based architectures, the proposed model leverages EfficientNetB4's compound scaling to optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, the Multi-Scale Attention Mechanism (utilizing [Formula: see text], and [Formula: see text] kernels) enhances feature representation by capturing tumor boundaries across different scales, addressing limitations of existing CNN-based segmentation methods. Our approach effectively suppresses irrelevant regions and enhances tumor localization through attention-enhanced skip connections and residual attention blocks. Extensive experiments were conducted on the publicly available Figshare brain tumor dataset, comparing different EfficientNet variants to determine the optimal architecture. EfficientNetB4 demonstrated superior performance, achieving an Accuracy of 99.79%, MCR of 0.21%, Dice Coefficient of 0.9339, and an Intersection over Union (IoU) of 0.8795, outperforming other variants in accuracy and computational efficiency. The training process was analyzed using key metrics, including Dice Coefficient, dice loss, precision, recall, specificity, and IoU, showing stable convergence and generalization. Additionally, the proposed method was evaluated against state-of-the-art approaches, surpassing them in all critical metrics, including accuracy, IoU, Dice Coefficient, precision, recall, specificity, and mean IoU. This study demonstrates the effectiveness of the proposed method for robust and efficient segmentation of brain tumors, positioning it as a valuable tool for clinical and research applications.