Paper Title
QUANTUM AI-DRIVEN HYBRID METHODOLOGY FOR BRAIN TUMOR STRATIFICATION: A SYNERGISTIC APPROACH

Abstract
Stratification of brain tumors with medical images is an important aspect of modern medicine and medical imaging where a rapid and accurate diagnosis impacts the patient's outcome. The paper offers a Quantum AI inspired hybrid strategy that combines the best out of two models: Quantum Vision Transformer (QViT) and Quantum Variational Classifier (QVC). Both models leverage quantum computing principles with classical deep learning to improve accuracy diagnostics. The QViT uses quantum attention when performing the analysis of the segmented MRI images, which is superior in relation to identifying relevant spatial relationships, while the QVC uses variational quantum circuits to optimize classification in high dimensional spaces. The results of the study in both binary and multi-class brain MRI datasets reveal that QViT was outstanding in binary classification with 99.20% validation accuracy, and that QVC had excellent results in multi-class with 96.11% accuracy. The synergistic approach to the study demonstrates that hybrid quantum-classical models have the potential to improve task-related performance in medical images, and that hybrid quantum-classical models will be implemented in future clinical applications out systematically toward integrating Quantum AI into diagnostic systems. Keywords - Brain Tumor Stratification, Hybrid Quantum-Classical Models, Medical Imaging, Quantum Artificial Intelligence, Quantum Variational Classifier, Quantum Vision Transformer