Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm
Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learn...
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ftdoajarticles:oai:doaj.org/article:b29c725b4c46451ba734f6092fbb1ff4 2024-09-15T18:25:45+00:00 Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm Lei Zhang Rongji Shi Naser Youssefi 2024-06-01T00:00:00Z https://doi.org/10.1016/j.heliyon.2024.e32077 https://doaj.org/article/b29c725b4c46451ba734f6092fbb1ff4 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2405844024081088 https://doaj.org/toc/2405-8440 2405-8440 doi:10.1016/j.heliyon.2024.e32077 https://doaj.org/article/b29c725b4c46451ba734f6092fbb1ff4 Heliyon, Vol 10, Iss 11, Pp e32077- (2024) Oral cancer Diagnosis Gated recurrent unit networks Northern goshawk optimization algorithm Recurrent neural networks Medical imaging Science (General) Q1-390 Social sciences (General) H1-99 article 2024 ftdoajarticles https://doi.org/10.1016/j.heliyon.2024.e32077 2024-08-05T17:49:08Z Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer. Article in Journal/Newspaper Northern Goshawk Directory of Open Access Journals: DOAJ Articles Heliyon 10 11 e32077 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Oral cancer Diagnosis Gated recurrent unit networks Northern goshawk optimization algorithm Recurrent neural networks Medical imaging Science (General) Q1-390 Social sciences (General) H1-99 |
spellingShingle |
Oral cancer Diagnosis Gated recurrent unit networks Northern goshawk optimization algorithm Recurrent neural networks Medical imaging Science (General) Q1-390 Social sciences (General) H1-99 Lei Zhang Rongji Shi Naser Youssefi Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
topic_facet |
Oral cancer Diagnosis Gated recurrent unit networks Northern goshawk optimization algorithm Recurrent neural networks Medical imaging Science (General) Q1-390 Social sciences (General) H1-99 |
description |
Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer. |
format |
Article in Journal/Newspaper |
author |
Lei Zhang Rongji Shi Naser Youssefi |
author_facet |
Lei Zhang Rongji Shi Naser Youssefi |
author_sort |
Lei Zhang |
title |
Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
title_short |
Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
title_full |
Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
title_fullStr |
Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
title_full_unstemmed |
Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm |
title_sort |
oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of northern goshawk optimization algorithm |
publisher |
Elsevier |
publishDate |
2024 |
url |
https://doi.org/10.1016/j.heliyon.2024.e32077 https://doaj.org/article/b29c725b4c46451ba734f6092fbb1ff4 |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Heliyon, Vol 10, Iss 11, Pp e32077- (2024) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2405844024081088 https://doaj.org/toc/2405-8440 2405-8440 doi:10.1016/j.heliyon.2024.e32077 https://doaj.org/article/b29c725b4c46451ba734f6092fbb1ff4 |
op_doi |
https://doi.org/10.1016/j.heliyon.2024.e32077 |
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Heliyon |
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10 |
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11 |
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e32077 |
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1810466226799902720 |