Abstract
Blockchain technology is critical in cyber security. The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits. Because of the ethical and legal implications of a patient's medical data, cyber security is a critical and challenging problem in healthcare. The image secrecy is highly vulnerable to various types of attacks. As a result, designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection. To resolve this issue, this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security (LGE-HES) algorithm for the cybersecurity of blockchain in healthcare networks. The blockchain algorithm preserves the security of the medical image by performing hash function. The execution of this research is carried out by MATLAB software. The suggested fra-mework was tested utilizing Computed Tumor (CT) pictures and MRI image data -sets, and the simulation results revealed the proposed model's profound implications. During the simulation, 94.9% of malicious communications were recognized and identified effectively, according to the total outcomes statistics. The suggested model's performance is also compared to that of standard approaches in terms of Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), time complexity, and other factors.