Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes

Background The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas. Methods A total of 183 patients with 215 lung adenocarcinomas were included in this study. A...

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Bibliographic Details
Main Authors: Wei Zhao, Wei Zhang, Yingli Sun, Yuxiang Ye, Jiancheng Yang, Wufei Chen, Pan Gao, Jianying Li, Cheng Li, Liang Jin, Peijun Wang, Yanqing Hua, Ming Li
Format: Article
Language:English
Published: Wiley 2019-10-01
Series:Thoracic Cancer
Subjects:
Convolution kernel
deep learning
iterative reconstruction
lung adenocarcinomas
radiomics
Online Access:https://doi.org/10.1111/1759-7714.13161

Internet

https://doi.org/10.1111/1759-7714.13161

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