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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
Descripción
Sumario: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).