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Enhancing Machine Learning Efficiency Through Secure Medical Image Sharing
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Author(s): M. Rajkumar (Vellore Institute of Technology, India), Niladri Maiti (School of Dentistry, Central Asian University, Tashkent, Uzbekistan), Riddhi Chawla (School of Dentistry, Central Asian University, Tashkent, Uzbekistan), Ripal Ranpara (Faculty of Computer Application, Marwadi University, India), R. Yogitha (Sathyabama Institute of Science and Technology, India), Pallavi Sagar Deshpande (Bharati Vidyapeeth, India)and Aakifa Shahul (SRM Medical College, India)
Copyright: 2025
Pages: 22
Source title:
Advanced Secure Transmission of Telemedicine-Based Bio-Medical Images
Source Author(s)/Editor(s): Binay Kumar Pandey (Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, India), A. Shaji George (TSM, Almarai Company, Saudi Arabia), Sameer Tiwari (George Mason University, USA), Salah A. Albermany (Kufa University, Iraq)and Ho Sy Hung (Hong Duc University, Vietnam)
DOI: 10.4018/979-8-3693-9821-0.ch007
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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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