Home > Published Issues > 2023 > Volume 14, No. 5, 2023 >
JAIT 2023 Vol.14(5): 941-949
doi: 10.12720/jait.14.5.941-949

Secure and Smart Teleradiology Framework Integrated with Technology-Based Fault Detection (CVT-FD)

Mustafa Sabah Mustafa 1, Mohammed Hasan Ali 2,3, Mustafa Musa Jaber 4,5, Amjad Rehman Khan 6,*, Narmine ElHakim 6, and Tanzila Saba 6
1. Department of Medical Instruments Engineering Techniques, Dijlah University College, Baghdad, Iraq;
Email: muustafa.sabah@duc.edu.iq (M.S.M.)
2. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-sadiq University, Baghdad 10021, Iraq; Email: mohammed.hasan@sadiq.edu.iq (M.H.A.)
3. College of Computer Science and Mathematics, University of Kufa, Najaf 540011, Iraq
4. Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10022, Iraq
5. Department of Medical Instruments Engineering Techniques, Al-Turath University College, Baghdad, Iraq;
Email: Mustafa.musa@duc.edu.iq (M.M.J.)
6. Artificial Intelligence and Data Analytics Lab CCIS Prince, Sultan University Riyadh, 11586, Saudi Arabia
*Correspondence: arkhan@psu.edu.sa (A.R.K.)

Manuscript received March 18, 2023; revised May 23, 2023; accepted June 6, 2023; published September 18, 2023.

Abstract—The healthcare sector has used cyber-physical systems to provide high-quality patient treatment. Many attack surfaces need sophisticated security solutions because of the wide range of medical devices, mobile devices, and body sensor nodes. Cyber-physical systems have various processing technologies, which means these technical methods are as varied. To reduce fraud and medical mistakes, restricted access to these data and fault authentication must be implemented. Because these procedures require information management about problem identification and diagnosis at a complex level distinct from technology, existing technologies must be better suited. This paper suggests a Computer Vision Technology-based Fault Detection (CVT-FD) framework for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behaviour can be predicted using an Artificial Neural Network (ANN), and analyzing this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.
Keywords—cyber-physical systems, healthcare, teleradiology, technological development

Cite: Mustafa Sabah Mustafa, Mohammed Hasan Ali, Mustafa Musa Jaber, Amjad Rehman Khan, Narmine ElHakim, and Tanzila Saba, "Secure and Smart Teleradiology Framework Integrated with Technology-Based Fault Detection (CVT-FD)," Journal of Advances in Information Technology, Vol. 14, No. 5, pp. 934-940, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.