Abstract
The neural architecture reached 98.25% accuracy, confirming strong discriminative performance and computational stability across folds.These findings support integration into a portable diagnostic prototype.Algorithmic validation establishes empirical reliability and translational potential of the proposed AI system.The framework delineates a pathway bridging algorithmic innovation, ethical governance and clinician-led deployment for equitable early breast cancer screening.