An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI

Mariana Pereira; Irene Fantini; Roberto Lotufo; Leticia Rittner

doi:10.1117/12.2550753

16 March 2020 An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI

Mariana Pereira, Irene Fantini, Roberto Lotufo, Leticia Rittner

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141V (2020) https://doi.org/10.1117/12.2550753
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Current techniques trying to predict Alzheimer's disease at an early-stage explore the structural information of T1-weighted MR Images. Among these techniques, deep convolutional neural network (CNN) is the most promising since it has been successfully used in a variety of medical imaging problems. However, the majority of works on Alzheimer's Disease tackle the binary classification problem only, i.e., to distinguish Normal Controls from Alzheimer's Disease patients. Only a few works deal with the multiclass problem, namely, patient classification into one of the three groups: Normal Control (NC), Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI). In this paper, our primary goal is to tackle the 3-class AD classification problem using T1-weighted MRI and a 2D CNN approach. We used the first two layers of ResNet34 as feature extractor and then trained a classifier using 64 × 64 sized patches from coronal 2D MRI slices. Our extended-2D CNN proposal explores the MRI volumetric information, by using non-consecutive 2D slices as input channels of the CNN, while maintaining the low computational costs associated with a 2D approach. The proposed model, trained and tested on images from ADNI dataset, achieved an accuracy of 68.6% for the multiclass problem, presenting the best performance when compared to state-of-the-art AD classification methods, even the 3D-CNN based ones.

Conference Presentation

Citation Download Citation

Mariana Pereira, Irene Fantini, Roberto Lotufo, and Leticia Rittner "An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141V (16 March 2020); https://doi.org/10.1117/12.2550753

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available