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A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area
We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243292/ https://www.ncbi.nlm.nih.gov/pubmed/35776652 http://dx.doi.org/10.1016/j.xpro.2022.101485 |
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author | Chen, Liuyin Qi, Haoyang Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Liang, Guoyuan Zhang, Zijun |
author_facet | Chen, Liuyin Qi, Haoyang Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Liang, Guoyuan Zhang, Zijun |
author_sort | Chen, Liuyin |
collection | PubMed |
description | We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022). |
format | Online Article Text |
id | pubmed-9243292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92432922022-07-01 A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area Chen, Liuyin Qi, Haoyang Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Liang, Guoyuan Zhang, Zijun STAR Protoc Protocol We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022). Elsevier 2022-06-22 /pmc/articles/PMC9243292/ /pubmed/35776652 http://dx.doi.org/10.1016/j.xpro.2022.101485 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Chen, Liuyin Qi, Haoyang Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Liang, Guoyuan Zhang, Zijun A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title | A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title_full | A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title_fullStr | A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title_full_unstemmed | A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title_short | A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
title_sort | deep learning based ct image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243292/ https://www.ncbi.nlm.nih.gov/pubmed/35776652 http://dx.doi.org/10.1016/j.xpro.2022.101485 |
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