| Autor: |
Ali A; School of IT, UNITAR International University, Petaling Jaya 47301, Malaysia., Ali H; Department of Computer System, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan., Saeed A; Department of Computer Science and IT, Jalozai Campus, UET Peshawar, Peshawar 25000, Pakistan., Ahmed Khan A; Department of Computer Science, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan., Tin TT; Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia., Assam M; Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan., Ghadi YY; Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi 122612, United Arab Emirates., Mohamed HG; Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Sep 07; Vol. 23 (18). Date of Electronic Publication: 2023 Sep 07. |
| DOI: |
10.3390/s23187740 |
| Abstrakt: |
The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
