Research Area: Machine Learning
Alzheimers disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients independence. There has been a growing focus on early AD detection using artificial intelligence. Convolutional neural networks (CNNs) have proven revolutionary for image-based applications and have been applied to brain scans. In recent years, studies have utilised two-dimensional (2D) CNNs on magnetic resonance imaging (MRI) scans for AD detection. To apply a 2D CNN on three-dimensional (3D) MRI volumes, each MRI scan is split into 2D image slices. A CNN is trained over the image slices by calculating a loss function between each subjects label and each image slices predicted output. Although 2D CNNs can discover spatial dependencies in an image slice, they cannot understand the temporal dependencies among 2D image slices in a 3D MRI volume. This study aims to resolve this issue by modelling the sequence of MRI features produced by a CNN with deep sequence-based networks for AD detection.
Keywords:
Alzheimers disease
Convolutional neural networks
Artificial Intelligence
Brain scans
Magnetic resonance imaging
Author(s) Name: Amir Ebrahimi, Suhuai Luo, Raymond Chiong
Journal name: Computers in Biology and Medicine
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.compbiomed.2021.104537
Volume Information: Volume 134
