Hybrid Classical–Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays

Cardiovascular diseases are among the major health problems that likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical–quantum (CQ) transfer models detect cardiomegaly CXRs. Using Qiskit and PennyLane, integrated parameterized circuit into classic network implemented PyTorch. We mined CheXpert public repository create balanced dataset with 2436 posteroanterior CXRs different patients distributed between control. k-fold cross-validation, CQ were trained using state vector simulator. normalized global effective dimension allowed us compare trainability run Qiskit. For prediction, ROC AUC scores up 0.93 accuracies 0.87 achieved several models, rivaling classical–classical (CC) model as reference. A trustworthy Grad-CAM++ heatmap hot zone covering heart was visualized more often QC option than CC (94% vs. 61%, p < 0.001), which may boost rate of acceptance by professionals.