Multi-Class Brain Tumor MRI Classification Using EfficientNet Based Hybrid Attention Network

Brain tumor diagnosis from magnetic resonance imaging (MRI) plays an important role in supporting early clinical assessment. However, distinguishing different tumor types remains challenging because of variations in tumor size, shape, and appearance across patients. This paper presents an enhanced EfficientNetV2B0-based model that combines the Convolutional Block Attention Module (CBAM) with Multi-Head Self-Attention (MHSA) to improve feature representation for multi-class brain tumor classification. The model is evaluated on a publicly available dataset containing 7,200 brain MRI images from four categories: glioma, meningioma, pituitary tumor, and no tumor. Its performance is compared with EfficientNetV2B0, EfficientNetV2B0 with CBAM, EfficientNetV2B0 with MHSA, ResNet50, DenseNet121, and MobileNetV3Small under the same experi- mental setting. The proposed model achieved a test accuracy of 96.30%, with a weighted F1-score of 96.29%, a mean Dice score of 96.36%, and a mean IoU of 93.02%, outperforming all baseline models. It also obtained macro and micro AUC values of 99.65% and 99.63%, respectively, demonstrating consistent clas- sification performance across all classes. These findings indicate that integrating channel attention with self-attention helps the network learn more discriminative features while preserving the computational efficiency of the EfficientNetV2B0 backbone.