Convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingChapter

34 Citations (Scopus)

Abstract

Convolutional neural networks (CNNs or ConvNets) are a popular group of neural networks that belong to a wider family of methods known as deep learning. The secret for their success lies in their carefully designed architecture capable of considering the local and global characteristics of the input data. Initially, CNNs have been designed to process image data efficiently, and for this, they were developed with properties such as local connectivity, spatial invariance, and hierarchical features. With these properties, CNNs have propelled breakthroughs across several research areas and have recently been applied in psychiatry and neurology to investigate brain disorders. In this chapter, we will present a theoretical introduction to CNNs. We will then illustrate their use in brain disorders by reviewing exemplary applications to neuroimaging and electroencephalogram data.

Original languageEnglish
Title of host publicationMachine Learning
Subtitle of host publicationMethods and Applications to Brain Disorders
PublisherElsevier
Pages173-191
Number of pages19
ISBN (Electronic)9780128157398
ISBN (Print)9780128157398
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Alzheimer’s disease
  • Brain disorders
  • Brain tumor
  • CNN
  • Convnet
  • Convolutional neural network
  • Deep learning
  • Electroencephalogram
  • Neuroimaging
  • Seizure

Fingerprint

Dive into the research topics of 'Convolutional neural networks'. Together they form a unique fingerprint.

Cite this