Residual and plain convolutional neural networks for 3D brain MRI classification

In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, although modern advancements in deep learning for image classification can provide a powerful framework for automatic feature generation and more straightforward analysis. In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures. We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimers Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans.

• Publication year

  2017

• Venue

  IEEE International Symposium on Biomedical Imaging

• Publication date

  2017-01-23

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1109/ISBI.2017.7950647arXiv 1701.06643
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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