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.

中文翻译：

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使用具有三元组损失函数的深层连体卷积神经网络对阿尔茨海默病进行四向分类

阿尔茨海默氏病 (AD) 是一种神经退行性疾病，会对多个大脑区域造成不可逆转的损害，包括海马体，从而导致认知、功能和行为受损。疾病的早​​期诊断将减少患者及其家属的痛苦。为实现这一目标，在本文中，我们提出了一种孪生卷积神经网络 (SCNN) 架构，该架构采用三重态损失函数将输入 MRI 图像表示为 k 维嵌入。我们使用预训练和非预训练的 CNN 将图像转换到嵌入空间。这些嵌入随后用于阿尔茨海默病的 4 向分类。使用 ADNI 和 OASIS 数据集测试模型功效，其准确率分别为 91.83% 和 93.85%。此外，