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

被引：18

作者：

Shaffi, Noushath ^{[1
]}

Hajamohideen, Faizal ^{[1
]}

Mahmud, Mufti ^{[2
,3
,4
]}

Abdesselam, Abdelhamid ^{[5
]}

Subramanian, Karthikeyan ^{[1
]}

Al Sariri, Arwa ^{[1
]}

机构：

[1] Univ Technol & Appl Sci Sohar, Dept Informat Technol, Sohar 311, Oman

[2] Nottingham Trent Univ, Dept Comp Sci, Nottingham NG11 8NS, England

[3] Nottingham Trent Univ, CIRC, Nottingham NG11 8NS, England

[4] Nottingham Trent Univ, MTIF, Nottingham NG11 8NS, England

[5] Sultan Qaboos Univ, Dept Comp Sci, Muscat 123, Oman

来源：

BRAIN INFORMATICS (BI 2022) | 2022年 / 13406卷

关键词：

Structural magnetic resonance imaging; Alzheimer's disease; Mild cognitive impairment; Triplet-loss; Siamese CNN;

D O I：

10.1007/978-3-031-15037-1_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：277 / 287

页数：11

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