A Secure COVID Affected CT Scan Image Encryption Scheme Using Hybrid MLSCM for IoMT Environment

Project Code :TCMAPY2016

Objective

The main objective of this project, titled “A Secure COVID-Affected CT Scan Image Encryption Scheme Using Hybrid MLSCM for IoMT Environment”, is to design and implement a highly secure and efficient image encryption system that ensures the privacy and integrity of sensitive medical data within IoMT-based healthcare environments. This project aims to develop a Hybrid Modified Logistic Sine Chaotic Map (MLSCM) algorithm capable of generating complex, unpredictable keys to achieve superior confusion and diffusion properties in the encryption process. By integrating the SHA-512 hashing algorithm, the system enhances data authentication and prevents unauthorized alterations or intrusions. The project also seeks to achieve high encryption speed, minimal computational overhead, and strong resistance against cryptographic attacks such as brute force, statistical, and differential attacks. Using Django as the web framework and SQL for secure database management, the system provides a scalable and user-friendly platform for encrypting and managing COVID-affected CT scan

Abstract

The rapid advancement of the Internet of Medical Things (IoMT) has revolutionized healthcare by enabling remote monitoring, diagnosis, and data sharing among connected medical devices. However, this connectivity also introduces serious security and privacy concerns, especially when dealing with sensitive medical images such as COVID-19–affected CT scans. Any unauthorized access, tampering, or leakage of these images can lead to severe ethical and legal implications. To address these issues, this project introduces a secure COVID-affected CT scan image encryption scheme using a Hybrid Modified Logistic Sine Chaotic Map (MLSCM) algorithm. The proposed model combines the strengths of logistic and sine chaotic maps to generate complex, highly unpredictable keys that enhance both confusion and diffusion processes in the encryption stage. This hybrid chaotic system ensures strong randomness and sensitivity to initial conditions, making brute-force or statistical attacks computationally infeasible. The encryption process transforms the original CT image into an encrypted image with uniform pixel distribution, thereby protecting it from unauthorized access during storage or transmission within IoMT networks. Additionally, the system achieves high encryption speed and low computational overhead, making it suitable for real-time medical applications where performance and accuracy are critical. Comparative experiments with existing methods show that the proposed hybrid MLSCM approach yields higher information entropy, lower correlation coefficients, and better robustness against noise and cropping attacks. Hence, the designed encryption scheme not only safeguards patient data privacy but also strengthens the security infrastructure of smart healthcare systems, ensuring trusted communication and reliable data protection in IoMT environments during and beyond the COVID-19 era.

Keywords:
COVID-19 CT Scan Images, Image Encryption, Hybrid Modified Logistic Sine Chaotic Map (MLSCM), Internet of Medical Things (IoMT), Data Security, Patient Privacy, SHA-512 Algorithm, Cryptography, Django Framework, SQL Database, Medical Image Protection, Chaotic Map, Secure Data Transmission, Smart Healthcare System.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.