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 journalpeer-review

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Author(s)

  • Haoyang Qi
  • Di Lu
  • Jianxue Zhai
  • Kaican Cai
  • Long Wang
  • Guoyuan Liang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number101485
Journal / PublicationSTAR Protocols
Volume3
Issue number3
Online published22 Jun 2022
Publication statusPublished - 16 Sep 2022

Link(s)

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 journalpeer-review

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