A Hybrid Quantum Computing and Artificial Intelligence Model for Secure Medical Image Classification and Cancer Prediction

Abstract

The process of medical image analysis to find early signs of cancer in patients has gained a lot of importance in enhancing diagnostic accuracy and survival rates of patients. Traditional medical image classification systems based on artificial intelligence also include drawbacks concerning the computational complexity, the sensitive aspects of the data security, and lesser prediction accuracy when working with large datasets in healthcare. Moreover, a key challenge in intelligent healthcare settings is the safe management of sensitive medical images. In solving these problems, this study offers a quantum computing and artificial intelligence system hybrid to be used in the secure medical image classification and cancer prediction. The proposed model combines quantum-enhanced computational learning as well as deep artificial intelligence methods to achieve better classification accuracy, fast feature optimization, and performance in predictions. A safe medical image processing software that uses encryption and privacy-conscious algorithms is followed to safeguard confidential healthcare data in data transfer and analysis. The framework utilizes advanced preprocessing, feature extraction, and hybrid quantum-AI classification approaches in order to identify tumors effectively and predict cancer. Benchmark medical imaging datasets of MRI, CT, and cancer histopathological images were used to evaluate the experiment experimentally. The classification and prediction metrics such as Accuracy, Precision, Recall, Specificity, F1-Score, AUC-ROC, PSNR, and SSIM were used to perform performance assessment. Experimentation has shown that the hybrid framework had a higher classification accuracy, high cancer prediction reliability, high image security, and low computational latency in comparison to the traditional deep learning methods. The suggested model has a huge potential of secure, intelligent and next generation healthcare diagnostic application.