QUANTUM-INSPIRED EVOLUTIONARY MODEL FOR ACCURATE AND ROBUST BRAIN TUMOR SEGMENTATION

Brain tumor segmentation has remained a critical task in medical image analysis, as accurate delineation has directly supported diagnosis, treatment planning, and clinical decision-making. Conventional deep learning approaches have achieved notable success; however, they have often struggled with limited robustness when facing heterogeneous tumor shapes, intensity variations, and imaging noise across multimodal MRI data. Existing segmentation frameworks have relied heavily on deterministic optimization strategies that have suffered from premature convergence and reduced generalization. These limitations have affected segmentation consistency, particularly in complex tumor boundaries and low-contrast regions, which have demanded adaptive and globally optimized solutions. This study has presented a quantum-inspired evolutionary framework that has integrated probabilistic quantum representation with an evolutionary optimization mechanism. The proposed framework has encoded segmentation candidates using quantum bits that have allowed superposition-based exploration of the solution space. An evolutionary update strategy has guided probability amplitudes toward optimal states, while a convolutional segmentation backbone has extracted hierarchical spatial features. A fitness-driven selection process has refined candidate solutions that have maximized region similarity and boundary accuracy. The training process has incorporated adaptive mutation and crossover operators that have preserved diversity and stability during convergence. Experimental evaluation demonstrates that the proposed method achieves superior performance on benchmark brain MRI datasets. The Dice similarity coefficient reaches 0.91–0.93 across modalities, while the Jaccard index ranges from 0.81– 0.84. Sensitivity achieves up to 0.92, and specificity remains high at 0.90–0.94. Overall accuracy ranges between 0.89–0.94, surpassing conventional CNN-based and evolutionary baselines. Visual inspection confirms precise tumor boundary delineation, particularly in infiltrative regions, indicating enhanced robustness under noise and intensity variations.