AUTHOR=Liu Manhua , Cheng Danni , Yan Weiwu ,  Alzheimer’s Disease Neuroimaging Initiative 

TITLE=Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 12 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00035

DOI=10.3389/fninf.2018.00035

ISSN=1662-5196

ABSTRACT=Alzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder which often happens in people aged more than 65 years old. Currently, there is no effective cure for AD, but it is of great interest to develop treatments that can delay its progression. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Positrons Emission Tomography (PET) is a functional molecular imaging modality, which proves to be a powerful tool to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. The success of these computer-aided diagnosis methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNN), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The hierarchical 2D CNNs are built to capture the intra-slice features while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for final classification. No rigid image registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance.