Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training

Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training

Medical imaging plays a critical role in contemporary healthcare, although it confronts issues relating to storage, security, and confidentiality in machine learning-based diagnostic systems. The proposed framework, Diegif, presents an efficient and safe mechanism for converting DICOM (Digital Imagi...

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Bibliographic Details
Main Authors: Abdullah Al Siam, Md Maruf Hassan, Md Atikur Rahaman, Masuk Abdullah
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Control and Optimization
Subjects:
DICOM
EGIF
ML
AES
Encryption
Decryption
Online Access:http://www.sciencedirect.com/science/article/pii/S2666720725000013
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