Using generative adversarial networks and parameter optimization of convolutional neural networks for lung tumor classification

Chun Hui Lin, Cheng Jian Lin, Yu Chi Li, Shyh Hau Wang

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.

原文English
文章編號480
頁(從 - 到)1-17
頁數17
期刊Applied Sciences (Switzerland)
11
發行號2
DOIs
出版狀態Published - 2021 一月 2

All Science Journal Classification (ASJC) codes

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程

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