NQNN: Noise-aware Quantum Neural Networks for Medical Image Classification

Noisy labels in high-dimensional, and multiclass medical im- age datasets pose a significant challenge for machine learning models. While hybrid quantum-classical architectures, such as quantum neural networks (QNNs), have shown promise in medical imaging, their ro- bustness under noisy label conditions remains largely unexplored. To address this gap, we propose a Noise-aware Quantum Neural Network (NQNN), integrating Fourier Attenuation, Reweight Estimation, and Adaptive Pooling to enhance feature extraction and classification ro- bustness. Fourier Attenuation filters high-frequency noise, Reweight Es- timation prioritizes cleaner labels based on uncertainty, and Adaptive Pooling dynamically refines feature aggregation. We evaluate NQNN on six benchmark medical datasets (PathMNIST, BloodMNIST, OrganAM- NIST, OrganCMNIST, OCTMNIST, and DermaMNIST) across noise ra- tios (10%, 30%, and 50%) and classification configurations (binary, four- class, and full multiclass). Comparative benchmarks against five QNN- based and two deep-learning baselines demonstrate NQNN’s superior performance, such that achieving 80.25% accuracy on organCMNIST at 10% noise and maintaining strong performance at higher noise ratios. Our ablation studies validate the effectiveness of each noise-handling mechanism, highlighting their complementary contributions to noise ro- bustness. By bridging quantum advancements with real-world medical di- agnostics, NQNN establishes a new benchmark for noise-resilient medical image classification, offering a scalable and adaptive quantum-classical learning framework.