Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Ghazal, T M, Khan, Muhammad Adnan, Nasir, Muhammad Umar, Zubair, Muhammad, Khan, Muhammad Farhan, Ahmad, Munir, Rahman, Atta-ur, Al Hamadi, Hussam and Mansoor, Wathiq (2022) Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning. Sensors, 22 (19). ISSN 1424-8220

Full text not available from this repository.

Abstract

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Affiliation: Skyline University College
SUC Author(s): Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924 and Khan, Muhammad Adnan
All Author(s): Ghazal, T M, Khan, Muhammad Adnan, Nasir, Muhammad Umar, Zubair, Muhammad, Khan, Muhammad Farhan, Ahmad, Munir, Rahman, Atta-ur, Al Hamadi, Hussam and Mansoor, Wathiq
Item Type: Article
Uncontrolled Keywords: kidney cancer; transfer learning; IoMT; deep learning; blockchain
Subjects: B Information Technology > BP Internet of Things
B Information Technology > BR Deep Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Mosys Team
Date Deposited: 25 Dec 2023 13:51
Last Modified: 25 Dec 2023 13:51
URI: https://research.skylineuniversity.ac.ae/id/eprint/641
Publisher URL: https://www.mdpi.com/1424-8220/22/19/7483
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/17524
Related URLs:

    Actions (login required)

    View Item
    View Item
    Statistics for SkyRep ePrint 641 Statistics for this ePrint Item