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Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm
When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body’s lymphatic system. Skin cancer is easier to diagnos...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919846/ https://www.ncbi.nlm.nih.gov/pubmed/35350832 http://dx.doi.org/10.1515/med-2022-0439 |
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author | Li, Gengluo Jimenez, Giorgos |
author_facet | Li, Gengluo Jimenez, Giorgos |
author_sort | Li, Gengluo |
collection | PubMed |
description | When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body’s lymphatic system. Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye. This makes us to provide an artificial intelligence-based methodology to diagnose this cancer with higher accuracy. This article proposes a new non-destructive testing method based on the AlexNet and Extreme Learning Machine network to provide better results of the diagnosis. The method is then optimized based on a new improved version of the Grasshopper optimization algorithm (GOA). Simulation of the proposed method is then compared with some different state-of-the-art methods and the results showed that the proposed method with 98% accuracy and 93% sensitivity has the highest efficiency. |
format | Online Article Text |
id | pubmed-8919846 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-89198462022-03-28 Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm Li, Gengluo Jimenez, Giorgos Open Med (Wars) Research Article When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body’s lymphatic system. Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye. This makes us to provide an artificial intelligence-based methodology to diagnose this cancer with higher accuracy. This article proposes a new non-destructive testing method based on the AlexNet and Extreme Learning Machine network to provide better results of the diagnosis. The method is then optimized based on a new improved version of the Grasshopper optimization algorithm (GOA). Simulation of the proposed method is then compared with some different state-of-the-art methods and the results showed that the proposed method with 98% accuracy and 93% sensitivity has the highest efficiency. De Gruyter 2022-03-11 /pmc/articles/PMC8919846/ /pubmed/35350832 http://dx.doi.org/10.1515/med-2022-0439 Text en © 2022 Gengluo Li and Giorgos Jimenez, published by De Gruyter https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. |
spellingShingle | Research Article Li, Gengluo Jimenez, Giorgos Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title | Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title_full | Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title_fullStr | Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title_full_unstemmed | Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title_short | Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
title_sort | optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919846/ https://www.ncbi.nlm.nih.gov/pubmed/35350832 http://dx.doi.org/10.1515/med-2022-0439 |
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