Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices

Gonzalo Farias, Ernesto Fabregas, Sebastian Dormido-Canto, Jesus Vega, Sebastian Vergara, Sebastian Dormido Bencomo, Ignacio Pastor, Alvaro Olmedo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach.

Original languageEnglish
Article number8537905
Pages (from-to)72345-72356
Number of pages12
JournalIEEE Access
StatePublished - 2018


  • Images classification
  • auto-encoder
  • future extraction
  • nuclear fusion


Dive into the research topics of 'Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices'. Together they form a unique fingerprint.

Cite this