Recent Advances in Brain MRI Image Classification for Cancer Detection Using Transformer and Group Parallel Axial Attention with Quantum Self-Attention: A Systematic Review
Abstract
Brain tumor detection using Magnetic Resonance Imaging (MRI) is a critical component of modern neuro-oncology, enabling early diagnosis and effective treatment planning. Traditional machine learning approaches often struggle with complex tumor structures and variability in imaging modalities, limiting their clinical applicability. Recent advancements in deep learning, particularly Transformer-based architectures, group parallel axial attention mechanisms, and emerging quantum self-attention models, have significantly improved classification accuracy and efficiency.
Transformer models utilize self-attention mechanisms to capture long-range dependencies in MRI images, achieving superior performance compared to conventional Convolutional Neural Networks (CNNs). Axial attention further enhances efficiency by decomposing attention operations across spatial dimensions, reducing computational complexity while preserving contextual information. Additionally, quantum self-attention introduces a novel paradigm by integrating quantum computing principles into deep learning frameworks, enabling enhanced feature representation and optimization.
This systematic review analyzes studies from 2020 to 2023, highlighting key trends, including hybrid CNN-Transformer architectures, attention optimization strategies, and explainable AI integration. Comparative analysis reveals that hybrid and attention-based models achieve classification accuracy exceeding 99%. Despite these advancements,…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.