TY - JOUR
T1 - Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function
AU - for the Alzheimer’s Disease Neuroimaging Initiative
AU - Hajamohideen, Faizal
AU - Shaffi, Noushath
AU - Mahmud, Mufti
AU - Subramanian, Karthikeyan
AU - Al Sariri, Arwa
AU - Vimbi, Viswan
AU - Abdesselam, Abdelhamid
N1 - Funding Information:
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). For up-to-date information, see http://adni-info.org/.
Funding Information:
NS, FH, KS, VV and AA were funded by the Ministry of Higher Education, Research and Innovation (MoHERI) of the sultanate of Oman under the Block Funding Program (Grant number-MoHERI/BFP/UoTAS/01/2021). MM was funded by Nottingham Trent University, UK, through the Strategic Research Theme 2022 grant.
Publisher Copyright:
© 2023, The Author(s).
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/2/17
Y1 - 2023/2/17
N2 - Alzheimer’s disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer’s disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.
AB - Alzheimer’s disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer’s disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.
KW - Alzheimer’s disease
KW - Classification
KW - MRI
KW - Mild cognitive impairment
KW - Siamese
KW - Triplet-loss
UR - http://www.scopus.com/inward/record.url?scp=85148323743&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85148323743&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/11366c7e-ad7f-3c82-9d00-50e4b1c9cd5a/
U2 - 10.1186/s40708-023-00184-w
DO - 10.1186/s40708-023-00184-w
M3 - Article
C2 - 36806042
AN - SCOPUS:85148323743
SN - 2198-4018
VL - 10
JO - Brain Informatics
JF - Brain Informatics
IS - 1
M1 - 5
ER -