https://jjcit.org/paper/286 10.5455/jjcit.71-1764868722 Nadia Shamsulddin Abdulsattar,Fatimah S. Abdulsattar Brain Tumor,MRI,ResNet50,Squeeze-and-Excitation (SE),CBAM 597 142 5-Dec.-2025 15-Feb.-2026 9-Mar.-2026 MRI image classification of brain tumors is critical for accurate and early diagnosis. New developments in deep learning have revealed that inserting attention mechanisms into convolutional neural networks can greatly improve classification performance. The ECA attention mechanism is also introduced in this study. This work assesses the effectiveness of Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) sequentially integrated with the ResNet50 model, which increases classification accuracy, precision and recall when compared to the basic model, according to experimental results on two datasets for brain tumors. The suggested model employs attention mechanisms to focus valuable information selectively and suppress irrelevant information. The experiments are conducted on two datasets (Brain Tumor MRI and Brisc). The first dataset displays great improvements over basic CNN models, with precision, recall, accuracy, F1 score and AUC at 0.9914, 0.9903, 0.9945, 0.9908 and 0.9989, respectively. The second dataset gives the results for precision, recall, accuracy, F1 score and AUC at 0.9860, 0.9857, 0.9860, 0.9858 and 0.9985, respectively. From these results, the importance of attention mechanisms in deep-learning models for medical imaging is highlighted, which suggests that SE and CBAM modules can be available as more dependable and effective instruments for brain-tumor classification in clinical settings. Future studies should investigate transformer-based and hybrid attention techniques to enhance automated brain tumor categorization.