The detection and classification of brain tumors are important yet challenging tasks in medical diagnostics. Traditional methods, primarily based on MRI analysis, face limitations due to the complexity of tumor presentations. We focus on incorporating quantum circuits within a hybrid quantum-classical neural network architecture, a promising yet underexplored approach to enhancing diagnostic tools. The study aims to unravel how varying entanglement designs in quantum convolutional neural networks contribute to MRI image classification. Employing the Kaggle brain tumor dataset Br35H, we benchmarked our quantum-classical integrated network against existing models. We observed significant feature extraction and processing efficiency improvements with minimal parameter increases. Our findings suggest that incorporating strongly entangled quantum circuits can significantly enhance the network’s capability for accurate brain tumor classification. In a clinical setting, the improved feature classification ability of quantum neural networks leads to quicker, more precise diagnostic processes, enabling prompt and suitable therapeutic responses.