Hybrid Quantum-Classical Convolutional Neural Networks for Scalable Optimization in Medical Image Segmentation
Keywords:
Convolutional neural network, Hybrid quantum computing, Medical image segmentation, Quantum machine learning, Scalable optimizationAbstract
The increased medical imaging data at exponential rates has posed unprecedented challenges to the conventional methods of image segmentation by traditional computational methods. This paper provides a conceptual model of Hybrid Quantum-Classical Convolutional Neural Networks (HQC-CNNs) that employ quantum computing concepts to overcome the problem of scalability in medical imaging segmentation problems. The proposed architecture is a system that incorporates quantum circuits in classical CNN models to add quantum entanglement and superposition to facilitate feature extraction and dimensionality reduction. The hybrid method does not disrupt the current medical imaging processes, but proposes quantum-enhanced optimization solutions. The conceptual model considers all of the major challenges, such as quantum noise suppression, optimisation of the classical-quantum interface, and scalability. This paper discusses the theoretical benefits of processing high-dimensional medical imaging data, possible computational speed-ups associated with quantum parallelism and possible ways of making this technology operational in clinical systems. The framework lays the basis for future empirical studies of quantum-enhanced deep learning models with medical image analysis in particular, and the potential to achieve better diagnostic accuracy and less computational resources in constrained healthcare settings.
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