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Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI

Yagis, Ekin and Garcia Seco De Herrera, Alba and Citi, Luca (2021) Convolutional Autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021-06-07 - 2021-06-09, Aveiro, Portugal.

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Abstract

Rapid and accurate diagnosis of Alzheimer's disease (AD) is critical for patient treatment, especially in the early stages of the disease. While computer-assisted diagnosis based on neuroimaging holds vast potential for helping clinicians detect disease sooner, there are still some technical hurdles to overcome. This study presents an end-to-end disease detection approach using convolutional autoencoders by integrating supervised prediction and unsupervised representation. The 2D neural network is based upon a pre-trained 2D convolutional autoencoder to capture latent representations in structural brain magnetic resonance imaging (MRI) scans. Experiments on the OASIS brain MRI dataset revealed that the model outperforms a number of traditional classifiers in terms of accuracy using a single slice.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - IEEE Symposium on Computer-Based Medical Systems
Uncontrolled Keywords: Alzheimer's Disease; Deep Learning; Image Classification; Autoencoder; MRI; Neuroimaging
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 02 Feb 2022 14:36
Last Modified: 23 Sep 2022 19:46
URI: http://repository.essex.ac.uk/id/eprint/31786

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