Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays

G. V. Eswara Rao, Rajitha Bakthula, Parvathaneni Naga Srinivasu, Muhammad Fazal Ijaz, Marcin Wozniak

Abstract: Highlights•A New Hybrid Custom CNN (CCNN) with a Quantum-Classical Network is proposed using lung chest X-ray images to identify respiratory disorders.•Encoding qubits, producing entangled quantum states, monitoring classical computer output, and expanding qubits optimise classical trainable parameters.•To demonstrate their efficacy, we compared MMS and MSMS to various state-of-art models like Mobile Net, VGG16, Inception Net, and ResNet50.•An actual quantum processor, IBMQ-QASM, tests the improved custom CNN Quantum-classical model, which is proven to handle the noisy constraints of retrieved features in images with a high resolution.•The experiment outperformed earlier models in qubits, qdepth, training, and testing accuracies, concerning various evaluation metrics.