An Unified Quantum Classical Model For Noisy Label Medical Image Binary Classification.

Abstract

The presence of noisy labels in medical imaging datasets can severely impact diagnostic accuracy, leading to incorrect predictions and reduced reliability. This challenge necessitates the development of robust classification methods capable of mitigating label noise and ensuring consistent performance. In this study, we propose a hybrid quantum-classical neural network (QNN-DNN) designed to enhance resilience against label noise by incorporating quantum-assisted feature processing. The model employs quantum circuits for feature transformation, enriching data representations before classification by a deep neural network (DNN). By leveraging the unique properties of quantum computation, such as entanglement and superposition, the approach effectively suppresses the adverse effects of mislabeling. The proposed framework is evaluated on OrganMNIST and PneumoniaMNIST, two widely used benchmark datasets in medical imaging. To systematically assess its robustness, symmetric label noise is introduced at 10%, 20%, and 30%. Experimental results indicate that the QNN-DNN model consistently outperforms classical convolutional networks (CNNs) and noise-robust classification methods, demonstrating superior accuracy under varying noise conditions. The integration of quantum feature encoding enhances representation learning, fostering better generalization and stability despite label inconsistencies. These findings underscore the potential of quantum-enhanced classification frameworks in addressing label noise challenges in medical image analysis. As quantum computing technology advances, this hybrid approach could serve as a foundation for more reliable, noise-resistant AI-driven diagnostic systems, ultimately improving patient outcomes and clinical decision-making