Hybrid Channel-Spatial Attention Enhanced EfficientNet for Multi-Class Brain Tumor Classification Using MRI
Brain tumor classification from Magnetic Resonance Imaging (MRI) remains challenging because of tumor heterogeneity, the overlapping intensity distribution of tumors, and the limited availability of medical images. This study introduces a lightweight deep learning model by integrating a Hybrid Channel–Spatial Attention (HCSA) module into the EfficientNet-B0 architecture for multi-class brain tumor classification. The HCSA module processes channels and spatial features sequentially to enhance feature representation by focusing on tumor-relevant information and better localizing the tumor region while maintaining computational efficiency. The proposed framework was evaluated on a publicly available Kaggle MRI brain tumor dataset, comprising 5,600 MRI images across 4 classes – glioma, meningioma, pituitary tumor and no-tumor. Comparative experiments were conducted with ResNet50, DenseNet121, EfficientNet-B0, and attention-enhanced EfficientNet models with identical preprocessing, data augmentation and training conditions. The proposed HCSA-EfficientNet-B0 model achieved a classification accuracy of 98.57%, an F1-score of 0.99, and an ROC-AUC value of 0.999 on the internal hold-out test set. Ablation results indicate that the combination of channel attention and spatial attention consistently outperforms each of the two attention mechanisms in isolation. Moreover, the proposed framework effectively captures the tumor-relevant regions, as suggested by Grad-CAM visualizations, which provide qualitative evidence supporting the interpretability of the classification results. The proposed framework is effective for multi-class brain tumor classification using MRI images and is able to improve classification performance while achieving computational efficiency.