Towards Alzheimer's Disease Classification through Transfer Learning
This addresses the challenge of limited training data for medical imaging in Alzheimer's Disease detection, though it is incremental as it applies existing transfer learning techniques to a specific domain.
The paper tackled the problem of Alzheimer's Disease classification from MRI images by using transfer learning with pre-trained models like VGG and Inception, achieving comparable or better performance with a training dataset almost 10 times smaller than state-of-the-art methods.
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.