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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number480
Pages (from-to)1-17
Number of pages17
JournalApplied Sciences (Switzerland)
Volume11
Issue number2
DOIs
Publication statusPublished - 2021 Jan 2

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Using generative adversarial networks and parameter optimization of convolutional neural networks for lung tumor classification'. Together they form a unique fingerprint.

Cite this