A frequency-driven quantum and graph-based method for robust brain tumor analysis

Brain tumor segmentation remains a significant challenge in medical image analytics due to the limited ability of current models to detect small lesions, capture spectral information, and represent anatomical context effectively. This study introduces the Frequency-Quantum-Graph Network (FQG-Net), an analytical framework that integrates quantum computing principles, adaptive frequency-domain processing, and graph-based contextual learning to enhance segmentation precision. The model employs quantum entanglement and superposition effects to enrich feature representation, an adaptive frequency enhancement mechanism to amplify tumor-specific spectral characteristics, and a graph neural contextual memory to preserve spatial and anatomical relationships. Multimodal MRI data are processed through selective quantum residual blocks that dynamically activate network components based on analytical requirements, ensuring both efficiency and stability. Empirical evaluations across multiple benchmark datasets demonstrate that FQG-Net delivers consistent improvements over state-of-the-art segmentation models, achieving higher accuracy, stronger generalization across datasets, and superior performance in detecting small and heterogeneous tumor regions. These findings highlight the analytical strength of quantum-enhanced deep learning and its potential to advance precision diagnostics in healthcare imaging. • Present an analytical model combining quantum learning and graph memory for brain tumor segmentation. • Develop an adaptive frequency module to enhance tumor feature detection in medical imaging. • Integrate quantum attention with contextual graph learning for precise lesion identification. • Employ multimodal imaging analytics to improve diagnostic accuracy and generalizability. • Demonstrate consistent cross-dataset performance, validating robustness in clinical analytics.