Hybrid Encryption Algorithms for 2D Medical Image Security and Anomaly Detection: A Novel Approach

Regulatory standards emphasize the need to safeguard medical images due to their sensitive nature, as they often contain Personally Identifiable Information (PII) and critical health data. If these images are breached, hackers can exploit them for identity theft, financial fraud, or extortion, leading to severe consequences for patients and healthcare providers. Additionally, tampered images may result in misdiagnoses and incorrect treatments, compromising clinical decision-making. As telemedicine expands and the volume of medical data surges, implementing strong security measures is vital to sustain trust in healthcare systems. Protecting medical images ensures patient privacy, bolsters healthcare credibility, and supports the delivery of quality care. This paper proposes seven robust hybrid encryption techniques assessed using metrics like MSE, PSNR, SSIM, entropy, edge preservation, histogram correlation, and encryption time. The findings reveal that these algorithms significantly strengthen security, detect anomalies, preserve image quality, and minimize encryption time, making them highly applicable for real-time telemedicine scenarios.