Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease

Noushath Shaffi*, Faizal Hajamohideen, Mufti Mahmud, Abdelhamid Abdesselam, Karthikeyan Subramanian, Arwa Al Sariri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationBrain Informatics - 15th International Conference, BI 2022, Proceedings
EditorsMufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031150364
Publication statusPublished - 2022
Event15th International Conference on Brain Informatics, BI 2022 - Virtual, Online
Duration: Jul 15 2022Jul 17 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13406 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Brain Informatics, BI 2022
CityVirtual, Online


  • Alzheimer’s disease
  • Mild cognitive impairment
  • Siamese CNN
  • Structural magnetic resonance imaging
  • Triplet-loss

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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