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Research Article |

Secure Medical Image Transmission based on Confidence-Adaptive Chaos Encryption (CACE) over Healthcare IoT Networks

Author(s): Zainab N. Abdulhameed 1, Haraa Raheem Hatem2, and Elaf H. Yahia11

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 3

Publisher : FOREX Publication

Published : 30 September 2026

e-ISSN : 2347-470X

Page(s) : 807-815

DOI: https://doi.org/10.37391/IJEER.140314

Abstract

Secure transmission of medical images over IoT networks requires strong encryption and maintenance of diagnostic value. To overcome this, this paper proposes an adaptive approach called Confidence-Adaptive Chaos Encryption (CACE) that is based on the SVM classifier confidence for choosing the strength of encryption for each image. Five rounds of the 4D chaotic Chen system are used for images classified as abnormal while only one round is used for those classified as normal with a high confidence. This is an adaptive method that performs a 19.65% decrease in the number of executed encryptions rounds relative to fixed five-round processing; end-to-end timing displayed no net latency benefit (the classifier overhead dominates), so the contribution is an adaptive cryptographic workload allocation instead of an acceleration. MRI scans for feature extraction by ResNet-50 are classified using an SVM classifier have an accuracy of 94.20% over 4000 MRI images with a five-fold cross-validation. As far as encryption is concerned, the system is able to obtain NPCR of 99.61% (fulfilling the requirement as 99.6093 %), UACI of 33.45 %, chi-square: 265.8, entropy of 7.9971 and key sensitivity is 99.62 % with the secret key consisting of four initial conditions (the fourth would be enough up to a nominal representation of 256 bits but not an effective representation of entropy figure). Reconstruction after Decryption is performed which gives infinity PSNR and 1.0 SSIM.All 800 test images were used to test the integrity of the diagnostic, and it was found that the SVM predictions remain intact through the encryption/decryption process and this has been validated by the equality of the SHA-256 cryptographic Hash of the test images before and after encryption and decryption. The results suggest that CACE can provide a secure medical image transmission system without any loss of medical image diagnostic information through the IoT.

Keywords: Medical Image Encryption, 4D Chaotic System, , Confidence-Adaptive Encryption, Brain Tumor Classification, Deep Feature Extraction.




Zainab N. Abdulhameed , Department of Electrical Engineering, University of Anbar, Iraq

Haraa Raheem Hatem , Department of Communication Engineering, University of Diyala, Iraq

Elaf H. Yahia, Department of Electrical Engineering, University of Anbar, Iraq

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Zainab N. Abdulhameed, Haraa Raheem Hatem, and Elaf H. Yahia (2026), Secure Medical Image Transmission based on Confidence-Adaptive Chaos Encryption (CACE) over Healthcare IoT Networks. IJEER 14(3), 807-815. DOI: 10.37391/IJEER.140314.