Deep Learning and Optimization Approaches in Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage: A Review

Abstract

The rapid expansion of IoT-enabled cloud storage in healthcare has improved the accessibility, storage, and exchange of medical images while introducing significant security concerns, including unauthorized access, data breaches, and cryptanalytic attacks. Protecting the confidentiality, integrity, and authenticity of sensitive medical data is therefore essential. Traditional cryptographic methods face limitations in addressing sophisticated cyber threats and large-scale image processing requirements. Consequently, deep learning, optimization techniques, and Quantum Neural Networks (QNNs) have emerged as promising approaches for secure medical image protection and cryptanalysis. Deep learning models can identify complex image patterns, generate robust encryption keys, and support efficient end-to-end encryption and decryption. Architectures such as convolutional neural networks, deep neural networks, and generative adversarial networks further strengthen medical image security in IoT-cloud environments. QNNs extend these capabilities by utilizing quantum principles, including superposition and entanglement, to efficiently process high-dimensional information. Qubit-based computation can accelerate cryptographic and optimization operations while improving security. Furthermore, hybrid quantum-classical frameworks combine deep learning capabilities with quantum optimization, offering scalable, computationally efficient, and resilient solutions for protecting medical images stored and transmitted through IoT-enabled cloud healthcare infrastructures.