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Quantum Computing for Cancer Diagnosis Optimization Using Medical Imaging and Neural Network Models

Khadija ShazlyHakan KhanNajaad OubeBlika
Volume 4

Abstract

The integration of quantum computing with neural network models offers a transformative approach to cancer diagnosis, particularly in the analysis of medical imaging data. This study proposes a novel diagnostic framework that leverages quantum computing to optimize deep neural networks for enhanced image classification and tumor detection. Medical imaging datasets, including MRI and CT scans, are processed using hybrid quantum-classical models to improve computational efficiency and diagnostic precision. Quantum machine learning algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Circuits (VQC), are employed to accelerate feature extraction and classification tasks. The neural network models are further refined using quantum-enhanced optimization techniques, leading to faster convergence and improved diagnostic accuracy. Experimental evaluations reveal that the proposed framework significantly outperforms classical models in detecting cancerous patterns with reduced computational overhead, offering promising implications for clinical oncology.

Keywords

Quantum computing, cancer diagnosis, medical imaging, neural networks, diagnostic optimization, quantum machine learning

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