Deep Feature–Driven Chaotic Encryption for Secure Medical Image Transmission A Comparative CNN-Based Framework
DOI:
https://doi.org/10.65419/albahit.v5i2.123Keywords:
Capacitor Switching, Energization Transient, Overvoltage, Inrush Current, ATP/EMTP, Power System Transients, Sensitivity Analysis, Back-to-Back SwitchingAbstract
Secure medical image transmission is a critical requirement in modern telemedicine. This paper proposes a robust hybrid encryption framework that integrates deep feature extraction with chaotic cryptography. Unlike conventional methods, encryption parameters are derived directly from high-level features extracted by pretrained CNNs (ResNet50, AlexNet, and MobileNetV2) to initialize a Logistic Map. This content-dependent key generation enhances resistance against brute-force and differential attacks. Experimental results across multiple modalities (X-ray, MRI, and Color images) demonstrate near-ideal security metrics: NPCR > 99.7% , UACI ≈ 33.3%, and Entropy ≈ 7.63. Robustness analysis confirms successful diagnostic recovery PSNR reached 22.92 dB under Salt-and-Pepper noise attacks, while occlusion recovery achieved 14.35 dB Comparative analysis reveals that while ResNet50 offers maximum sensitivity, MobileNetV2 achieves a 36.5% reduction in execution time, making it ideal for real-time clinical applications. The framework, implemented via MATLAB App Designer, provides secure and computationally efficient encryption with lossless decryption under ideal conditions for next-generation healthcare infrastructures.
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