Encryption-then-Compression System for Cloud-based Medical Image Services
Published in IEEE International Conference on Information Networking (ICOIN), 2022
Abstract
In the recent years, encryption-then-compression (EtC) algorithms are becoming popular for multimedia applications due to their low computational requirements and format compliancy with the JPEG image standard. In this study, we first extend the applications of EtC algorithms to medical image processing domain. Then, given importance of medical data, we propose a method to improve the security performance of the conventional EtC schemes with acceptable degradation in the compression savings but without compromising quality of the recovered images. We have implemented a deep learning (DL) model for tuberculosis (TB) screening in Montgomery datasets. Our analysis shows that the recovered images are of high quality and has no adverse effect on the accuracy of the DL model for TB detection. The proposed method preserves the crucial information in the image necessary for correct diagnosis while significantly reducing the image size and providing security to enable telemedicine application of eHealth services.