Hybrid Quantum CNN-ResNet152 Model for Enhanced Brain Tumor Diagnosis

Abstract

Deep learning and quantum computing are used in the Hybrid Quantum CNN-ResNet152 Model for Enhanced Brain Tumor Diagnosis to make MRI-based brain tumor categorization more accurate.  There is room for improvement in the performance of the commonly used traditional CNN and ResNet architectures (ResNet 50, ResNet 101, ResNet 152) in medical imaging.  A comparison of various models in this research shows that CNN-ResNet152 outperforms them all with a score of 96.19% F1-score, 97.75% recall, 94.67% precision, and 89.8% accuracy.  Data imbalance, computational complexity, and model generalization are still issues, however.  One way to tackle this is by incorporating quantum computing methods like QCNN, QVC, and QFE into feature extraction and optimization processes.  Hybrid quantum-classical models seem to be a game-changer in the field of medical artificial intelligence and quantum computing, since they considerably enhance brain tumor detection, leading to quicker diagnoses with less ambiguity and better patient outcomes.