Towards Alzheimer's Disease Classification through Transfer Learning

Understand

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years.

• Recent success of deep learning in computer vision have progressed such research further.
• However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks.
• In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images.