APPLICATIONS OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) IN MEDICAL IMAGE SECURITY - (:unkn) unknown (My University, 2025)
The rapid growth of digital healthcare systems has led to an increasing reliance on medical imaging for diagnosis and treatment, raising significant concerns regarding data security and patient privacy. Convolutional Neural Networks (CNNs), a class of deep learning algorithms renowned for their exceptional performance in image analysis, have emerged as powerful tools for enhancing medical image security. This abstract explores the diverse applications of CNNs in securing medical images, including encryption, authentication, watermarking, and anomaly detection. CNNs can learn complex patterns and features, enabling robust image encryption techniques that protect data against unauthorized access and tampering. Additionally, CNN-based watermarking ensures data integrity and authentication without compromising image quality, while their ability to detect subtle anomalies helps identify potential security breaches. The integration of CNNs in medical image security not only improves data protection but also ensures compliance with stringent healthcare regulations, safeguarding patient confidentiality in the digital era. This paper highlights recent advancements, challenges, and future directions in leveraging CNNs for comprehensive medical image security solutions.