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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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000013 |
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| _version_ | 1832583029929803776 |
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| author | Abdullah Al Siam Md Maruf Hassan Md Atikur Rahaman Masuk Abdullah |
| author_facet | Abdullah Al Siam Md Maruf Hassan Md Atikur Rahaman Masuk Abdullah |
| author_sort | Abdullah Al Siam |
| collection | DOAJ |
| description | 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 Imaging and Communications in Medicine) data into EGIF (Encrypted Graphics Interchange Format) files to overcome these challenges. The framework comprises four key components: (1) converting DICOM files to GIF format with encryption, (2) decrypting EGIF files for processing, (3) enabling confidentiality-preserving machine learning training using EGIF data, and (4) facilitating physician diagnosis and report generation based on trained machine learning models. The Diegif framework aims to enhance storage efficiency by decreasing file sizes by 66.32%, thereby improving data transport efficacy and cloud storage affordability while preserving strong encryption for data confidentiality. Pseudocode algorithms are provided for each phase, ensuring reproducibility and transparency. This paper illustrates the framework’s potential to medical image processing, secure storage, and AI-driven diagnostic functions in healthcare. |
| format | Article |
| id | doaj-art-7b64b982d56a47ea875a64b2f64d5a8c |
| institution | Kabale University |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-7b64b982d56a47ea875a64b2f64d5a8c2025-01-29T05:02:16ZengElsevierResults in Control and Optimization2666-72072025-03-0118100515Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning trainingAbdullah Al Siam0Md Maruf Hassan1Md Atikur Rahaman2Masuk Abdullah3Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Dhaka, BangladeshDepartment of Computer Science & Engineering, Southeast University, Tejgaon, Dhaka, 1208, BangladeshSchool of Economics and Management, Jiujiang University, 551 Qianjin Donglu, Jiujiang, 332005, Jiangxi, PR ChinaDepartment of Vehicles Engineering, Faculty of Engineering, University of Debrecen, Ótemető street. 2-4, 4028, Debrecen, Hungary; Corresponding author.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 Imaging and Communications in Medicine) data into EGIF (Encrypted Graphics Interchange Format) files to overcome these challenges. The framework comprises four key components: (1) converting DICOM files to GIF format with encryption, (2) decrypting EGIF files for processing, (3) enabling confidentiality-preserving machine learning training using EGIF data, and (4) facilitating physician diagnosis and report generation based on trained machine learning models. The Diegif framework aims to enhance storage efficiency by decreasing file sizes by 66.32%, thereby improving data transport efficacy and cloud storage affordability while preserving strong encryption for data confidentiality. Pseudocode algorithms are provided for each phase, ensuring reproducibility and transparency. This paper illustrates the framework’s potential to medical image processing, secure storage, and AI-driven diagnostic functions in healthcare.http://www.sciencedirect.com/science/article/pii/S2666720725000013DICOMEGIFMLAESEncryptionDecryption |
| spellingShingle | Abdullah Al Siam Md Maruf Hassan Md Atikur Rahaman Masuk Abdullah Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training Results in Control and Optimization DICOM EGIF ML AES Encryption Decryption |
| title | Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training |
| title_full | Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training |
| title_fullStr | Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training |
| title_full_unstemmed | Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training |
| title_short | Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training |
| title_sort | diegif an efficient and secured dicom to egif conversion framework for confidentiality in machine learning training |
| topic | DICOM EGIF ML AES Encryption Decryption |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000013 |
| work_keys_str_mv | AT abdullahalsiam diegifanefficientandsecureddicomtoegifconversionframeworkforconfidentialityinmachinelearningtraining AT mdmarufhassan diegifanefficientandsecureddicomtoegifconversionframeworkforconfidentialityinmachinelearningtraining AT mdatikurrahaman diegifanefficientandsecureddicomtoegifconversionframeworkforconfidentialityinmachinelearningtraining AT masukabdullah diegifanefficientandsecureddicomtoegifconversionframeworkforconfidentialityinmachinelearningtraining |