Manuscript received June 17, 2025; revised July 5, 2025; accepted August 13 , 2025; published January 16, 2026.

Abstract—This paper addresses the dual challenges of efficient compr ession and secure transmission for medical images, par -ticularly in band width-constrained telemedicine scenarios, like tele-radiography. We propose an end-to-end pipeline combining deep learning-based compr ession with chaos-based encryption. A Convolutional Autoencoder (CAE), optimized with a Structural Similarity Index Measure (SSIM) loss function and incorporat-ing residual connections and batch normalization, achieves an 8:1 (87.5%) compr ession ratio on Chest X-ray images while maintaining a high fidelity of 96% SSIM and 36 dB Peak Signal-to-Noise Ratio (PSNR). To secure the compact latent representation generated by the CAE, we introduce a lightweight, chaos-based encryption scheme operating directly on the latent space. This scheme utilizes a logistic map for confusion and secure permutations for diffusion. The experimental results confirm the effectiveness of the compression module in preserving high-frequency details and the encryption scheme’ s resistance against statistical attacks, by achieving high entr opy (7.92), strong randomness (0.99), without correlation (close to 0 in horizontal, vertical, and diagonal directions), and very sensiti ve to small changes in the key (1 single bit change conduct to a completely different k eystream). Our work offers a promising solution for secure and efficient transmission of medical images over constrained networks.

Keywords—convolutional autoencoder, chaotic cryptosystem, image compression, protected latent space, SSIM, secure transmission

Cite: Rayane Cherifi, Mahdi Madani*, El-Bay Bourennane, and Karima Amara, "Secure and Efficient Tele-Radiography Based on the Fusion of a Convolutional Autoencoder and Chaotic Latent Encryption," Journal of Image and Graphics, Vol. 14, No. 1, pp. 49-57, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.