Secure eHealth: A Secure eHealth System to Detect COVID Using Image Steganography

COVID is a pandemic which has spread to all parts of the world. Detection of COVID infection is crucial to prevent the spread further. Contactless health care systems are essential which can be implemented with Cloud computing. Privacy and security of the medical image data transferred through untrusted channels cannot be ensured. The main aim is to secure the medical details when transferring them from the end device to the cloud and vice versa using image steganography. The medical lung images are masked under a normal and natural cover images

Stego-eHealth: An eHealth System for Secured Transfer of Medical Images Using Image Steganography

COVID pandemic has necessitated the need for virtual and online health care systems to avoid contacts. The transfer of sensitive medical information including the chest and lung X-ray happens through untrusted channels making it prone to many possible attacks. This paper aims to secure the medical data of the patients using image steganography when transferring through untrusted channels. A deep learning method with three parts is proposed – preprocessing module, embedding network and the extraction network. Features from the cover image and the secret image are extracted by the preprocessing module. The merged features from the preprocessing module are used to output the stego image by the embedding network. The stego image is given as the input to the extraction network to extract the ingrained secret image. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are the evaluation metrics used. Higher PSNR value proves the higher security; robustness of the method and the image results show the higher imperceptibility. The hiding capacity of the proposed method is 100% since the cover image and the secret image are of the same size

A novel Conceptual Machine Learning Method using Random Conceptual Decomposition

Formal Concept Analysis (FCA) is emerging in Data Science because of its generality, simplicity, and powerful mathematical foundation. It enabled a uniform data clustering methods into structured space of formal concepts. Several FCA based machine learning (ML) methods gave competitive results compared to classical methods. In another side, ensemble approach proved to be effective by aggregating different basic ML methods. Randomness improved other ML approaches. In this paper, we propose a new conceptual ML method by using random conceptual decomposition. This method integrated and experimented in the context of ensemble learning methods, gave encouraging good results, in general.ACKNOWLEDGMENT This contribution was made possible by NPRP grant #07-794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu