Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes

Upul Senanayake, Arcot Sowmya, Laughlin Dawes, Nicole A. Kochan, Wei Wen, Perminder Sachdev

Abstract

Timely intervention in individuals at risk of dementia is often emphasized, and Mild Cognitive Impairment (MCI) is considered to be an effective precursor to Alzheimers disease (AD), which can be used as an intervention criterion. This paper attempts to use deep learning techniques to recognise MCI in the elderly. Deep learning has recently come to attention with its superior expressive power and performance over conventional machine learning algorithms. The current study uses variations of auto-encoders trained on neuropsychological test scores to discriminate between cognitively normal individuals and those with MCI in a cohort of community dwelling individuals aged 70-90 years. The performance of the auto-encoder classifier is further optimized by creating an ensemble of such classifiers, thereby improving the generalizability as well. In addition to comparable results to those of conventional machine learning algorithms, the auto-encoder based classifiers also eliminate the need for separate feature extraction and selection while also allowing seamless integration of features from multiple modalities.

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Paper Citation


in Harvard Style

Senanayake U., Sowmya A., Dawes L., Kochan N., Wen W. and Sachdev P. (2017). Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 655-662. DOI: 10.5220/0006246306550662


in Bibtex Style

@conference{icpram17,
author={Upul Senanayake and Arcot Sowmya and Laughlin Dawes and Nicole A. Kochan and Wei Wen and Perminder Sachdev},
title={Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={655-662},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006246306550662},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes
SN - 978-989-758-222-6
AU - Senanayake U.
AU - Sowmya A.
AU - Dawes L.
AU - Kochan N.
AU - Wen W.
AU - Sachdev P.
PY - 2017
SP - 655
EP - 662
DO - 10.5220/0006246306550662