Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network

TL;DR

This paper introduces a multi-modality 3D CNN approach that fuses MRI and PET images around the hippocampus for accurate Alzheimer's diagnosis, eliminating manual feature extraction and achieving high classification accuracy.

Contribution

The study presents a novel multi-modality 3D CNN framework that automatically learns features from MRI and PET images for AD diagnosis, outperforming traditional methods.

Findings

Achieved up to 90.10% accuracy in NL/AD classification.

Demonstrated hippocampal region suffices for AD diagnosis.

CNN-based approach does not require image segmentation.

Abstract

Alzheimer's Disease (AD) is one of the most concerned neurodegenerative diseases. In the last decade, studies on AD diagnosis attached great significance to artificial intelligence (AI)-based diagnostic algorithms. Among the diverse modality imaging data, T1-weighted MRI and 18F-FDGPET are widely researched for this task. In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, and utilizes the stateof-art 3D image-processing CNNs to learn features for the diagnosis and prognosis of AD. To validate the performance of the proposed network, we trained the classifier with paired T1-MRI and FDG-PET images using the ADNI datasets, including 731 Normal (NL) subjects, 647 AD subjects, 441 stable MCI (sMCI) subjects and 326 progressive MCI (pMCI) subjects. We obtained the maximal accuracies of 90.10% for NL/AD task, 87.46% for NL/pMCI task, and 76.90% for sMCI/pMCI task. The proposed framework yields comparative results against state-of-the-art approaches. Moreover, the experimental results have demonstrated that (1) segmentation is not a prerequisite by using CNN, (2) the hippocampal area provides enough information to give a reference to AD diagnosis. Keywords: Alzheimer's Disease, Multi-modality, Image Classification, CNN, Deep Learning, Hippocampal