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

by   Yechong Huang, et al.

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 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


Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images

Structural MRI and PET imaging play an important role in the diagnosis o...

A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images

Background: Parkinson's disease (PD) is a prevalent long-term neurodegen...

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring...

Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN

There is a need for automatic diagnosis of certain diseases from medical...

Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis

Accurate diagnosis and prognosis of Alzheimer's disease are crucial to d...

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment...

Dilated deeply supervised networks for hippocampus segmentation in MRI

Tissue loss in the hippocampi has been heavily correlated with the progr...

Please sign up or login with your details

Forgot password? Click here to reset