Abstract
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. Earlier diagnosis of the disease will reduce the suffering of the patients and their family members. Towards that aim, this paper presents a Siamese Convolutional Neural Network (CNN) based model using the Triplet-loss function for the 4-way classification of AD. We evaluated our models using both pre-trained and non-pre-trained CNNs. The models’ efficacy was tested on the OASIS dataset and obtained satisfactory results under a data-scarce real-time environment.
This work is 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).
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Shaffi, N., Hajamohideen, F., Mahmud, M., Abdesselam, A., Subramanian, K., Sariri, A.A. (2022). Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds) Brain Informatics. BI 2022. Lecture Notes in Computer Science(), vol 13406. Springer, Cham. https://doi.org/10.1007/978-3-031-15037-1_23
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