Quantum-Enhanced Cancer Detection for Histopathologic Images

This paper investigates quantum image processing for histopathological cancer detection, focusing on enhanced feature extraction and improved classification. We propose a hybrid quantum-classical framework in which quantum preprocessing circuits, specifically, Quantum Hadamard Edge Detection (QHED) and the Quantum Fourier Transform (QFT), extract subtle spatial and spectral features from hematoxylin and eosin-stained whole-slide images. These quantum-enhanced features are then classified using deep neural networks such as ResNet-50, VGG-16, and UNet. Experiments on the PatchCamelyon dataset demonstrate that quantum-enhanced preprocessing increases sensitivity in detecting cancerous regions, while integrating hybrid preprocessing with an SVM classifier further improves classification accuracy. Although quantum noise remains a challenge, our results highlight the promise of quantum image pre-processing and feature extraction for advancing histopathological image classification.