Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease

TL;DR

This paper introduces a transfer learning approach with class decomposition using VGG19 and ResNet50 architectures to improve early Alzheimer's detection from sMRI images, achieving state-of-the-art accuracy.

Contribution

It combines transfer learning and class decomposition techniques with entropy-based image selection for enhanced Alzheimer's disease classification.

Findings

Achieved a 3% accuracy increase over previous methods.

Demonstrated effectiveness of transfer learning with class decomposition.

Identified most informative images using entropy-based technique.

Abstract

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.