Abstract
Artificial intelligence (AI) has quickly integrated into contemporary healthcare organizations, providing a variety of purposes, including identifying diseases, individualized therapy suggestions, and healthcare picture analytics. However, substantial security concerns are raised by the extensive use of AI in Healthcare, particularly regarding the reliability and confidentiality of private patient information. In this paper, we offer quantum photonic convolutional neural network (QP-CNN) to strengthen AI-based healthcare systems' security to overcome this crucial issue. To create an innovative convolutional neural network (CNN) architecture capable of handling the quantum computations that can endanger the safety of patient data, we analyze the special aspects of quantum photonics in this study. We explore the basic ideas behind quantum photonic computation and show how they can be used to develop strong cryptography methods for securing healthcare information during transport and preservation. Initially, we gather the dataset and preprocess the collected data using min–max normalization to remove duplicates and guarantee homogeneity. The subsequent stage involves utilizing principal component analysis (PCA) to extract pertinent features from the preprocessed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. In terms of accuracy (82.67%), precision (82%), recall (75.84%), and F1-score (85%), specificity (95.67%), AUC (93.15%). Our results show that the QP-CNN technique outperforms other methods in effectiveness. Our suggested QP-CNN technique provides exciting outcomes for the security of an artificial intelligence-based healthcare system.
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KSK: conceptualization, SG: methodology, FA: software, MYA: data curation, writing—original, AAZ: draft preparation, SLAH: visualization, investigation, supervision, MA: software, validation, writing- reviewing and editing.
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Sita Kumari, K., Shivaprakash, G., Arslan, F. et al. Research on the quantum photonic convolutional neural network for artificial intelligence-based healthcare system security. Opt Quant Electron 56, 149 (2024). https://doi.org/10.1007/s11082-023-05574-2
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DOI: https://doi.org/10.1007/s11082-023-05574-2