http://iet.metastore.ingenta.com
1887

Medical magnetic resonance imaging and spectroscopy

More general concepts than this:

1-1 of 1 results

Filter by subject:

< Any subject

  • Physics [1]
  • Cross-disciplinary physics and related areas of science and technology [1]
  • Biophysics, medical physics, and biomedical engineering [1]
  • Biophysics of neurophysiological processes [1]
  • Medical and biomedical uses of fields, radiations, and radioactivity; health physics [1]
  • Medical magnetic resonance imaging and spectroscopy [1]
  • Biomedical engineering [1]
  • Patient diagnostic methods and instrumentation [1]
  • Electrical and electronic engineering [1]
  • General topics, engineering mathematics and materials science [1]
  • See more See less

Filter by content type:

Filter by publication date:

< Any publication date

  • 2015 [1]

Filter by author:

< Any author

Discrimination of ADHD children based on Deep Bayesian Network
Author(s): A.J. Hao, B.L. He, C.H. Yin
Source: 2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015), 2015, p. 6 . - 6 .
Type: ConferencePaper
+ Show details - Hide details

Attention deficit hyperactivity disorder (ADHD) is a threat for the public health all the time, so the effective discrimination of it is significant and meaningful. In current research, Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD. In this paper, we introduce the Deep Bayesian Network, a combination of Deep Belief Network and Bayesian Network, to classify the ADHD children from the normal. In Deep Bayesian Network, The Deep Belief Network is applied to normalize and reduce dimension of the fMRI data in every brodmann area. And the Bayesian Network is used to extract the feature of relationships between several well-performed brain areas by structure learning. According to the information of structure and probability in Bayesian Network, we predicted the subjects as control, combined, inattentive or hyperactive using SVM classifier. The final results perform better than using single Deep Belief Network and the best results in ADHD-200 competition.

Add to favourites
Export citations
Items per page 20 | 50 | 100
Print this page
This is a required field
Please enter a valid email address