Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network

TL;DR

This paper introduces a deep 3D convolutional neural network that learns to classify Alzheimer's disease from brain MRI scans, effectively capturing anatomical features and adapting across different datasets.

Contribution

It presents a novel 3D-CNN architecture based on a pre-trained autoencoder for AD diagnosis, demonstrating improved accuracy and domain adaptability.

Findings

Outperforms conventional classifiers in accuracy on CADDementia dataset

Successfully generalizes features to ADNI dataset

Operates without skull-stripping preprocessing

Abstract

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer\{'}s disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification. Experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset.