Towards Alzheimer's Disease Classification through Transfer Learning

TL;DR

This paper demonstrates that transfer learning using pre-trained deep neural networks can effectively classify Alzheimer's Disease from MRI data with significantly less training data, achieving comparable or superior results.

Contribution

It introduces a transfer learning approach with pre-trained models and image entropy-based slice selection to improve AD classification with limited data.

Findings

Achieved comparable or better accuracy with ten times less training data.

Used image entropy to select informative MRI slices.

Validated approach on the OASIS dataset.

Abstract

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. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.