Enriching Image Security in Healthcare Based on Cryptography and Deep Learning Techniques

Securing electronic health records in the internet of medical things is a key interest in health-care due to the sector's different surroundings. As technology evolves, preserving the privacy, reliability, and accessibility of healthcare data becomes extremely challenging. Cryptographic techniques provide a possible solution for safeguarding confidential information about medical pictures while it is being transferred and stored. On the other hand, deep leaning has the potential to totally change cryptography by providing strong encryption, quality improvements, and detection potential for healthcare picture security. To increase the privacy of healthcare image information, this research analysed the fusion of deep leaning and cryptography methods. It gives a study of the current situation of deep learning-based image detection of anomalies approaches in working contexts, such as network typologies, supervision levels, along with evaluation norms. This study provides direction to future research techniques to overcome these problems, also the possibility and challenges of medical picture cryptography and picture detection of abnormality. This work bridges the gap between deep learning and encryption, paving the way for better privacy, integrity, and availability of key image data.