Abstract
Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. Due to their similar clinical symptoms, they are easily misdiagnosed as each other even with sophisticated clinical guidelines. For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. Recent advances in deep learning have delivered some of the best performance for medical image recognition tasks. However, its application to the differential diagnosis of AD and FTD pathology has not been explored.Approach: In this study, we proposed a novel deep learning based framework to distinguish between brain images of normal aging individuals and subjects with AD and FTD. Specifically, we combined the multi-scale and multi-type MRI-base image features with Generative Adversarial Network data augmentation technique to improve the differential diagnosis accuracy.Results: Each of the multi-scale, multitype, and data augmentation methods improved the ability for differential diagnosis for both AD and FTD. A 10-fold cross validation experiment performed on a large sample of 1,954 images using the proposed framework achieved a high overall accuracy of 88.28%.Conclusions: The salient contributions of this study are three-fold: (1) our experiments demonstrate that the combination of multiple structural features extracted at different scales with our proposed deep neural network yields superior performance than individual features; (2) we show that the use of Generative Adversarial Network for data augmentation could further improve the discriminant ability of the network regarding challenging tasks such as differentiating dementia sub-types; (3) and finally, we show that ensemble classifier strategy could make the network more robust and stable.
Highlights
As the first and third most common forms of dementia, Alzheimer’s disease (AD) (Association et al, 2011) and Frontotemporal dementia (FTD) (Bang et al, 2015) are often mistaken as each other
Patterns of brain atrophy observed in T1-weighted Magnetic Resonance Imaging (MRI) have been successfully used to capture structural changes in the human brain (Du et al, 2007; Davatzikos et al, 2011), for using in developing computational systems that can identify the type of dementia pathology in the brain
Various structural biomarkers have been explored to distinguish between AD and FTD, such as gray matter (GM) volume loss (Rabinovici et al, 2008), cortical thinning (Du et al, 2007), highdimensional features based on GM and white matter (WM) volume distribution of whole brain (Davatzikos et al, 2008), as well as atrophy and shape deformity of individual structures (Looi et al, 2010)
Summary
As the first and third most common forms of dementia, Alzheimer’s disease (AD) (Association et al, 2011) and Frontotemporal dementia (FTD) (Bang et al, 2015) are often mistaken as each other. This is due to the similarities in their clinical presentation, cognitive domains impairment, brain atrophy, and progressive alterations in language ability, behavior, and personality (Neary et al, 2005; Alladi et al, 2007; Womack et al, 2011). Various structural biomarkers have been explored to distinguish between AD and FTD, such as gray matter (GM) volume loss (Rabinovici et al, 2008), cortical thinning (Du et al, 2007), highdimensional features based on GM and white matter (WM) volume distribution of whole brain (Davatzikos et al, 2008), as well as atrophy and shape deformity of individual structures (Looi et al, 2010)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
