Deep Learning and Optimization Approaches in Brain MRI Image Classification for Cancer Detection Using Transformer and Group Parallel Axial Attention with Quantum Self-Attention: A Review

Abstract

Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in medical diagnostics, requiring high precision and reliability. Recent advancements in deep learning have significantly improved the accuracy of brain tumor classification, particularly through the use of transformer-based architectures and attention mechanisms. This review explores state-of-the-art deep learning and optimization approaches for brain MRI image classification, focusing on transformer models, group parallel axial attention, and quantum self-attention mechanisms. Transformers enable global feature extraction by modeling long-range dependencies, while axial attention reduces computational complexity by decomposing attention into spatial dimensions. Group parallel axial attention further enhances performance by processing multiple attention groups simultaneously, improving feature representation. Additionally, quantum self-attention introduces novel optimization capabilities by leveraging quantum-inspired principles for enhanced learning efficiency. The review covers recent literature from 2020 to 2023, highlighting improvements in classification accuracy, robustness, and computational efficiency. Benchmark datasets such as BraTS are widely used for evaluation. Despite significant progress, challenges such as data scarcity, model interpretability, and computational overhead persist. This study provides a comprehensive analysis of current methods, comparative insights, and future research directions for developing reliable and efficient brain tumor classification systems.