P1-313: Longitudinal Brain Structure Changes in Healthy/MCI Patients: A Deep Learning Approach for The Diagnosis and Prognosis of Alzheimer’s Disease

2016 ◽  
Vol 12 ◽  
pp. P543-P543
Author(s):  
Peng Dai ◽  
Femida Gwadry-Sridhar ◽  
Michael Bauer ◽  
Michael Borrie ◽  
Xue Teng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Brain Structure ◽  
Learning Approach ◽  
Diagnosis And Prognosis ◽  
Structure Changes

scholarly journals Applying a deep learning approach focusing on spatiotemporal features in early diagnosis of Alzheimer's disease

2021 ◽  
Vol 17 (S12) ◽  
Author(s):  
Eyitomilayo Yemisi Babatope ◽  
Jesus Alejandro Acosta‐Franco ◽  
Mireya Saraí García‐Vázquez ◽  
Alejandro Álvaro Ramírez‐Acosta ◽  
APIM Laboratory Citedi‐IPN
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Approach ◽  
Spatiotemporal Features

scholarly journals Predicting Alzheimer’s disease progression using multi-modal deep learning approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Garam Lee ◽  
◽  
Kwangsik Nho ◽  
Byungkon Kang ◽  
Kyung-Ah Sohn ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Learning Approach

scholarly journals A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

NeuroImage ◽  
2019 ◽  
Vol 189 ◽  
pp. 276-287 ◽  
Author(s):  
Simeon Spasov ◽  
Luca Passamonti ◽  
Andrea Duggento ◽  
Pietro Liò ◽  
Nicola Toschi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Approach

scholarly journals MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3243 ◽  
Author(s):  
Nagaraj Yamanakkanavar ◽  
Jae Young Choi ◽  
Bumshik Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Brain Structure ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Learning Methods ◽  
The Brain

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

scholarly journals Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

2020 ◽  
Vol 24 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Francisco J. Martinez-Murcia ◽  
Andres Ortiz ◽  
Juan-Manuel Gorriz ◽  
Javier Ramirez ◽  
Diego Castillo-Barnes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Learning Approach ◽  
Manifold Structure
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