AI-Optimized Autoencoder Architecture for Secure and Efficient Brain MRI Image Encryption and Compression on EDGE Platforms

• Jins Sebastian,
• J. Deny,
• S. N. Kumar,

Vol. 19, No. 10, October 31, 2025
10.3837/tiis.2025.10.004, Download Paper (Free):

• Convolutional Autoencoder
• DWT
• Edge Computing
• MRI Image Compression
• Spectral Data Augmentation
• Secure Medical Imaging

Abstract

The advancements in imaging techniques have improved the diagnostics in health care and medicine. The magnitude of MRI volumetric data sets remains a challenge with storage space, real-time processing, and communication, especially in edge computing environments that have limited resources. To tackle these issues, we suggest a new artificial intelligence-based optimisation of an autoencoder network architecture that combines spectral data augmentation with feature extraction using discrete wavelet transform on the brain MRI images for data compression and encryption. The system is optimised for edge deployment on NVIDIA Jetson TX2 platforms, allowing real-time processing at low power consumption. The proposed convolutional autoencoder encodes and decodes the MRI images while minimizing the reconstruction loss. It uses spectral data augmentation to improve overfitting and generalisation, while DWT further improves compression efficiency through multi-resolution feature extraction. The proposed model's compression performance and image reconstruction accuracy are evaluated with various parameters, achieving a compression ratio of 33.25 and a compression factor of 0.0625 with a mean squared error of 0.000445, a peak signal-to-noise ratio of 33.51 dB. Additionally, a structural similarity index of 0.9956 confirms that the model reconstructs images with a high preservation of detail, which is necessary for capturing critical diagnostic data. The deployment of the developed model on the NVIDIA Jetson TX2 GPU platform emphasizes computational efficiency and real-time execution, making the system suitable for telemedicine, remote diagnostics, and embedded AI healthcare applications.

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