  Early diagnosis of Alzheimer's disease (AD) is essential in preventing the
disease's progression. Therefore, detecting AD from neuroimaging data such as
structural magnetic resonance imaging (sMRI) has been a topic of intense
investigation in recent years. Deep learning has gained considerable attention
in Alzheimer's detection. However, training a convolutional neural network from
scratch is challenging since it demands more computational time and a
significant amount of annotated data. By transferring knowledge learned from
other image recognition tasks to medical image classification, transfer
learning can provide a promising and effective solution. Irregularities in the
dataset distribution present another difficulty. Class decomposition can tackle
this issue by simplifying learning a dataset's class boundaries. Motivated by
these approaches, this paper proposes a transfer learning method using class
decomposition to detect Alzheimer's disease from sMRI images. We use two
ImageNet-trained architectures: VGG19 and ResNet50, and an entropy-based
technique to determine the most informative images. The proposed model achieved
state-of-the-art performance in the Alzheimer's disease (AD) vs mild cognitive
impairment (MCI) vs cognitively normal (CN) classification task with a 3\%
increase in accuracy from what is reported in the literature.
