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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...

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Detalles Bibliográficos
Autores principales: Chen, Liuyin, Qi, Haoyang, Lu, Di, Zhai, Jianxue, Cai, Kaican, Wang, Long, Liang, Guoyuan, Zhang, Zijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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).
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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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