Enhancing Machine Learning Efficiency Through Secure Medical Image Sharing

Abstract

The development of machine learning applications in the healthcare industry depends on safe medical image sharing. Protecting patient privacy is more difficult but necessary as medical imaging data grows. This paper investigates how to integrate encryption techniques and secure data-sharing methods, like blockchain, to facilitate cross-organizational collaboration while maintaining data privacy. Sensitive patient data is kept in local systems thanks to collaborative models like federated learning, which enable institutions to train machine learning algorithms on decentralized datasets. This strategy makes use of the variety of data available across healthcare facilities while protecting the privacy of medical records. The creation of machine learning models for diagnosis and treatment planning that are more reliable and accurate is made possible by the safe interchange of medical images.

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