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Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Ahmed S. Almasoud1, Abdelzahir Abdelmaboud2, Faisal S. Alsubaei3, Manar Ahmed Hamza4,*, Ishfaq Yaseen4, Mohammed Abaker5, Abdelwahed Motwakel4, Mohammed Rizwanullah4

1 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 12435, Saudi Arabia
2 Department of Information Systems, College of Science and Arts, King Khalid University, Mahayil Asir, 62529,Saudi Arabia
3 Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959,Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, 16278, Saudi Arabia
5 Department of Computer Science, Community College, King Khalid University, Mahayil Asir, 62529, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2022, 72(2), 2633-2648.


Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector. The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector. Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique. Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security. Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client. For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image. Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance. A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique. The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.


Cite This Article

A. S. Almasoud, A. Abdelmaboud, F. S. Alsubaei, M. Ahmed Hamza, I. Yaseen et al., "Deep learning with image classification based secure cps for healthcare sector," Computers, Materials & Continua, vol. 72, no.2, pp. 2633–2648, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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