Brain tumors are highly invasive neurological diseases, and accurate early diagnosis is crucial for developing personalized treatment plans. Computer-aided diagnosis (CAD) based on deep learning techniques has achieved significant progress in medical image analysis, but limitations remain in terms of classification accuracy, computational efficiency, and interpretability. To address these issues, this study proposes an optimized EfficientNet model based on transfer learning and fine-tuning strategies. The model improves certain convolutional and fully connected layers and adds a global average pooling layer and a Dropout layer at the top of the network to enhance feature extraction capability and classification performance. Additionally, gradient-weighted class activation mapping (Grad-CAM) is introduced to visualize the model′s decision-making process, effectively highlighting key discriminative regions of brain tumors, thereby improving interpretability and clinical reliability. Experimental results on the Figshare dataset demonstrate that the proposed model achieves an accuracy of 99.35% on the test set while significantly reducing parameter count and computational complexity, outperforming baseline models including VGG16, ResNet152V2, and Vision Transformer across all major metrics. Furthermore, cross-dataset validation shows that the model attains an accuracy of 92.51%, further demonstrating its robust stability and generalization capability.