Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network

Fahad F. Alruwaili; Bayan Alabduallah; Hamed Alqahtani; Ahmed S. Salama; Gouse Pasha Mohammed; Amani A. Alneil

doi:10.1109/ACCESS.2023.3304269

IEEE Access (Jan 2023)

Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network

Fahad F. Alruwaili,
Bayan Alabduallah,
Hamed Alqahtani,
Ahmed S. Salama,
Gouse Pasha Mohammed,
Amani A. Alneil

Affiliations

Fahad F. Alruwaili: Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
Bayan Alabduallah: ORCiD; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
Hamed Alqahtani: Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
Ahmed S. Salama: Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, Egypt
Gouse Pasha Mohammed: ORCiD; Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Amani A. Alneil: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

DOI: https://doi.org/10.1109/ACCESS.2023.3304269
Journal volume & issue: Vol. 11
pp. 87583 – 87591

Abstract

Read online

Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords