Quantum-Enhanced Dual-Backbone Architecture for Accurate Gastrointestinal Disease Detection Using Endoscopic Imaging

Background: Quantum machine learning (QML) holds significant promise for advancing medical image classification. However, its practical application to large-scale, high-resolution datasets is constrained by the limited number of qubits and the inherent noise in current quantum hardware. Methods: In this study, we propose the Fused Quantum Dual-Backbone Network (FQDN), a novel hybrid architecture that integrates classical convolutional neural networks (CNNs) with quantum circuits. This design is optimized for the noisy intermediate-scale quantum (NISQ), enabling efficient computation despite hardware limitations. We evaluate FQDN on the task of gastrointestinal (GI) disease classification using wireless capsule endoscopy (WCE) images. Results: The proposed model achieves a substantial reduction in parameter complexity, with a 29.04% decrease in total parameters and a 94.44% reduction in trainable parameters, while outperforming its classical counterpart. FQDN achieves an accuracy of 95.80% on the validation set and 95.42% on the test set. Conclusions: These results demonstrate the potential of QML to enhance diagnostic accuracy in medical imaging.