A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Article number | 101485 |
Journal / Publication | STAR Protocols |
Volume | 3 |
Issue number | 3 |
Online published | 22 Jun 2022 |
Publication status | Published - 16 Sep 2022 |
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DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85132726432&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(b5ba1150-322a-4dc8-a352-b812d678f7a5).html |
Abstract
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).
Research Area(s)
- Bioinformatics, Biotechnology and bioengineering, Cancer, Computer sciences, Health Sciences, Systems biology
Citation Format(s)
A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area. / Chen, Liuyin; Qi, Haoyang; Lu, Di et al.
In: STAR Protocols, Vol. 3, No. 3, 101485, 16.09.2022.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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