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 language | English |
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Title of host publication | Machine Learning |
Subtitle of host publication | Methods and Applications to Brain Disorders |
Publisher | Elsevier |
Pages | 173-191 |
Number of pages | 19 |
ISBN (Electronic) | 9780128157398 |
ISBN (Print) | 9780128157398 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- Alzheimer’s disease
- Brain disorders
- Brain tumor
- CNN
- Convnet
- Convolutional neural network
- Deep learning
- Electroencephalogram
- Neuroimaging
- Seizure