Accurate diagnosis of brain tumors from MRI scans is crucial for effective treatment planning and patient outcomes. Deep learning techniques have shown promise in automating this process, but the interpretability of their decisions remains a challenge. This paper introduces the Squeeze-and-Excitation Convolutional Neural Network-MobileNet (SECNN-MNet), a novel deep learning model designed for precise and interpretable classification of brain tumors in MRI images. SECNN-MNet combines the robust feature extraction capabilities of SE-CNN with the computational efficiency of MobileNet. By integrating SE blocks, the model dynamically recalibrates feature maps to capture crucial tumor characteristics, enhancing diagnostic accuracy. MobileNet’s depthwise separable convolutions optimize computational resources while preserving image detail fidelity. Our model achieves exceptional performance …