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.
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.
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.
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) |
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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: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 02 Feb 2022 14:36 |
Last Modified: | 30 Oct 2024 20:49 |
URI: | http://repository.essex.ac.uk/id/eprint/31786 |
Available files
Filename: CBMS_2021.pdf