Brain tumor classification has now become an important diagnosis in the medical field. Human evaluation of MRI scans is time-consuming and prone to medical error, which requires accurate and efficient computational models to assist physicians. Recently, hybrid deep learning models have emerged as a promising technique that combines the strengths of multiple architectures to enhance classification performance. This paper investigates the role of attention mechanisms in MRI-based tumor classification based on three different models: the baseline, which is EfficientNetB0; an enhanced model using simple attention, AttentiveEfficientNetB0; and a proposed hybrid model that integrates a channel attention mechanism, ChannelEfficientNetB0. ChannelEfficientNetB0 enhances EfficientNetB0 by incorporating a channel attention module that recalibrates feature maps by adaptively weighting diagnostically critical channels, consequently increasing computational efficiency. Attention mechanisms are extensively utilized in domains such as natural language processing and computer vision. This study uses the Brain Tumor MRI Dataset with 7,023 images having four classes, including glioma, meningioma, no tumor and pituitary, divided into training and test folders for model training and evaluation. The proposed ChannelEfficientNetB0 model achieved training and validation accuracy of 99.89% and 99.54%, respectively. The model significantly reduced diagnostic errors, with zero false negatives in meningioma, no tumor, and pituitary cases, and highly minimized false negatives in glioma. This study shows that incorporating channel attention mechanism significantly boosts the model’s ability and has an impactful result in MRI-based diagnosis. These results highlight the efficacy of channel attention mechanisms that amplify tumor-specific characteristics, demonstrating their potential to reduce diagnostic errors in clinical workflows and providing a pathway to a reliable diagnosis of brain tumors.