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Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning
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 t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572837/ https://www.ncbi.nlm.nih.gov/pubmed/36236584 http://dx.doi.org/10.3390/s22197483 |
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author | Nasir, Muhammad Umar Zubair, Muhammad Ghazal, Taher M. Khan, Muhammad Farhan Ahmad, Munir Rahman, Atta-ur Hamadi, Hussam Al Khan, Muhammad Adnan Mansoor, Wathiq |
author_facet | Nasir, Muhammad Umar Zubair, Muhammad Ghazal, Taher M. Khan, Muhammad Farhan Ahmad, Munir Rahman, Atta-ur Hamadi, Hussam Al Khan, Muhammad Adnan Mansoor, Wathiq |
author_sort | Nasir, Muhammad Umar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9572837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95728372022-10-17 Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning Nasir, Muhammad Umar Zubair, Muhammad Ghazal, Taher M. Khan, Muhammad Farhan Ahmad, Munir Rahman, Atta-ur Hamadi, Hussam Al Khan, Muhammad Adnan Mansoor, Wathiq Sensors (Basel) Article 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. MDPI 2022-10-02 /pmc/articles/PMC9572837/ /pubmed/36236584 http://dx.doi.org/10.3390/s22197483 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nasir, Muhammad Umar Zubair, Muhammad Ghazal, Taher M. Khan, Muhammad Farhan Ahmad, Munir Rahman, Atta-ur Hamadi, Hussam Al Khan, Muhammad Adnan Mansoor, Wathiq Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title | Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title_full | Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title_fullStr | Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title_full_unstemmed | Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title_short | Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning |
title_sort | kidney cancer prediction empowered with blockchain security using transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572837/ https://www.ncbi.nlm.nih.gov/pubmed/36236584 http://dx.doi.org/10.3390/s22197483 |
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